Choose the kind of contribution, then prepare the evidence it needs. Below, each category has a compact synthesis, observed layout context, a possible section order and an explicit artifact package. All 223 oral studies have a primary category; mixed papers retain secondary classifications in the maintained data.
Observed counts and editorial recommendations are deliberately separate. “Start with one architecture diagram” is a drafting suggestion—not an ICLR requirement or an inference that every accepted architecture paper has exactly one. A theorem is appropriate only if there is a real mathematical claim to prove.
Corpus-wide numbers and all-paper ledger · Inline examples of 12 visual forms · Cross-cutting writing patterns
The central deliverable changes the computational organization, representation or reusable model component. Training and efficiency can be secondary contributions.
Corpus context · 40 papers: median 5 main figures and 4 main tables; body span ≈ 10.11 pages; attached appendix span ≈ 8.89 pages (39 resolved boundaries). Separate source files: median 18.5 whole-file pages among 2 papers; these can repeat main text and are not appendix-only lengths. Extraction-assisted observations—not drafting targets.
Selected studies make a named representational or computational bottleneck visible, map components to that bottleneck, and separate the evidence for component choices from broad benchmark performance. The strongest examples distinguish what is available in training and at inference.
Editorial starting package: choose artifacts that test your actual claims. Quantities below are planning suggestions, not counts inferred from all category members or conference rules.
Start with one
Show input/output, frozen/trainable components, the changed path, and training-only signals. Use an inset for deployment if the paths differ.
When: Core artifact when the changed computation is the main contribution.
One worked example or small qualitative grid
Make the representation operational before large equations or module names.
When: Especially useful for unfamiliar tokens, geometric objects or multimodal interfaces.
One compact, grouped table
Give datasets, metric direction, matched resources, provenance and uncertainty near the numbers.
When: Use multiple focused tables if tasks have incompatible protocols.
One ablation table or panel plus a diagnostic when needed
Test whether the claimed component solves the named failure rather than merely increases capacity.
When: Use matched controls; do not require a plot when a table communicates the result better.
One algorithm only when the procedure is not recoverable from the diagram/equations
Specify iteration, branching, state and gradient flow.
When: Not obligatory for a simple feed-forward module.
Only for an actual formal claim
Place assumptions and guarantee together and point to the proof.
When: An architectural paper does not need an invented theorem to resemble an oral paper.
Layer/configuration tables, tensor dimensions, training data and schedules, full task results, additional ablations and failure examples. Keep the defining computation in the main paper.
Architecture colors preserve modality identity; adjacent tables change the budget contract explicitly.
Three bottlenecks map to three changes and distinct evaluation axes; do not credit every gain to one component.
Separate what to predict from how much architecture is needed, then ablate target and backbone independently.
Boundary: These examples do not establish a universal architecture-paper figure count or prove that a larger diagram makes a stronger contribution.
See inline examples of these artifact styles · Compare every member in the measurement ledger
The primary reading route follows the annotated argument: Motivation and background → Method and controlled evaluation → Limitations and appendix. The opening makes sequence length a concrete scaling obstacle rather than using a generic multimodal-capability claim.
The primary reading route follows the annotated argument: Motivation, related work and background → Four-module world model → Prediction, ablation and policy experiments. The introduction contrasts semantic language units with fixed image patches through a small visual example, then positions the contribution as an extension of DLP/DDLP rather than hiding its lineage.
The primary reading route follows the annotated argument: Masking versus partitioning → Training, sampling, and Partition Transformer → Language, image, and complementary-mask experiments → Limitations, protocols, and computational costs. The core argument identifies a specific wasted operation—processing mask tokens at every step—rather than presenting speed as an unexplained headline. Prior accelerators are distinguished by which flexibility or quality they sacrifice.
The primary reading route follows the annotated argument: Representation matched to physical synthesis → Vocabulary, model, iterative reasoning → Property editing and makeability. The word-versus-character analogy makes block tokenization accessible, but the long introduction's societal and human-reasoning claims dilute the specific technical argument.
The primary reading route follows the annotated argument: Failure case, alternative target, analytic recovery → Real and synthetic benchmarks, ablations, limitations. Figures 1–3 distinguish normal-map appearance from recovered geometry, so the proposed intermediate shading target addresses a visible failure rather than a vaguely defined accuracy gap.
The primary reading route follows the annotated argument: Three deficiencies of aligned conceptual knowledge → Knowledge construction, matching and re-ranking. The introduction enumerates three concrete limitations, and the contribution bullets mirror them; this makes the component-to-problem mapping easy to follow.
The primary reading route follows the annotated argument: Cognitive motivation, energy intuition, training/inference algorithms → Learning scaling versus inference scaling. Use one visual mechanism across modalities, separate training and inference algorithms, and report absolute metrics plus actual computation alongside scaling slopes.
The primary reading route follows the annotated argument: Motivating experiments → Representation, data, benchmark, deployment. Three limitations—single relation type, static state, and missing task relevance—map directly to the proposed representation's design choices.
The primary reading route follows the annotated argument: Representation comparison → Description search, encoder training and object composition → Research-question experiments. Rather than opening with a new backbone, the paper asks which intermediate representation should connect brain signals and an image generator. The representation comparison supplies a rationale for the method.
The primary reading route follows the annotated argument: Three obstacles to unified audio intelligence → UALM-Gen → UALM → UALM-Reason → Evaluation and detailed recipes. The introduction explicitly identifies three technical challenges and then assigns each to a named model stage, making a large systems paper navigable.
The primary reading route follows the annotated argument: Introduction / Related Work → Approach → Experiments → Appendices A–H. The cognitive analogy supplies a memorable motivation, but a footnote explicitly disclaims neuroscientific replication. The technical problem becomes conflicting reference-image, audio and semantic guidance, not a claim that the avatar has an actual mind.
The primary reading route follows the annotated argument: Introduction / Related Work → Preliminaries / Method → Experiments / Conclusion → Appendices A–C. The opening distinguishes temporal from channel dependencies and ties its proposed module to the latter. Defining subject-independent splitting before the architecture makes the evaluation unit visible before presenting large sample counts.
The primary reading route follows the annotated argument: Depth Anything 3 → Visual geometry benchmark → Sufficiency ablations. Two questions—what to predict and how much architecture is necessary—organize the introduction. The contribution is framed around removing task-specific redundancy, then tested through separate target and backbone ablations.
The primary reading route follows the annotated argument: Methodology → Experiments → Discussion. The introduction positions a specific bottleneck between a single compressed vector and hundreds of patch vectors. This establishes why learned compact token sets matter before naming the nested training recipe.
The primary reading route follows the annotated argument: Dispatch MDP → Architecture and two-stage training → Simulation, transfer and ablations. The provocative title is usefully qualified in its first-page footnote: the final controller is single-agent, but MARL pretraining remains essential. This prevents the framing from erasing a dependency that the ablation later confirms.
The primary reading route follows the annotated argument: Unify two design paradigms → Base generation, search and extensions. The introduction challenges the generative-versus-hallucination dichotomy and uses an analogy to language/image inference-time scaling, while explaining the protein-specific data bottleneck.
Mixed contribution: A pretrained generator, data construction and inference-time search are combined; architecture is primary without denying the other artifacts.
The primary reading route follows the annotated argument: Efficiency bottlenecks lead to targeted components → Different deployment regimes receive separate evidence. The opening connects high token counts to practical training and inference barriers, motivating architectural changes rather than treating parameter count as the only efficiency metric.
The primary reading route follows the annotated argument: Temporal-consistency motivation → Data-generating assumptions and T-SAEs → Evaluation and applications. The opening offers competing explanations for shallow SAE features, then argues for a testable method-design explanation rather than concluding that language models lack semantic information.
The primary reading route follows the annotated argument: Unification problem and related work → Method and data pipeline → Benchmark, comparisons, transfer and ablations. The paper presents architecture and data as coupled obstacles, then gives each its own method section instead of attributing all improvement to one module.
The primary reading route follows the annotated argument: Representation bottleneck → Two-stage mechanism → Component evidence before task evidence. The introduction makes the representation the unit of novelty: storing each shape in network weights does not scale. Two named design changes then connect that bottleneck to a compact latent representation rather than merely announcing a new generative model.
The primary reading route follows the annotated argument: Capability gap and background → Architecture, objectives, protocols → Capabilities followed by controlled explanations. The contribution list separates representation coverage, prompt awareness and architectural choices. Each becomes a visible experimental question, making a multi-part multimodal system easier to evaluate than a single broad claim of universality.
The primary reading route follows the annotated argument: Three bottlenecks, three changes → Principle to recurrence to implementation → Distinct evaluation axes. The introduction repeatedly maps each limitation to one mechanism and one empirical claim: discretization to quality, complex states to tracking, and MIMO to memory-bound decoding. This repeated structure makes a technically dense three-part contribution easier to follow.
The primary reading route follows the annotated argument: Motivation and geometric construction → Pretraining and adaptation → Experiments and technical appendices. The argument distinguishes learning shared structure across graph domains from adapting to a held-out domain, giving the method two explicit responsibilities.
The primary reading route follows the annotated argument: Projection-layer construction → Benchmarks and motion planning. The contribution is carefully positioned as a specialized implicit layer, not the invention of implicit differentiation; exploiting projection structure explains the practical novelty.
Mixed contribution: The core deliverable is an implicit projection layer with numerical and optimization machinery; readers may also reasonably browse it as an optimization algorithm.
The primary reading route follows the annotated argument: Camera-dependent ambiguity and robustness diagnosis → Camera-aware representation → Evaluation and analysis. The focal-length/object-size/depth relationship supplies an unusually concrete opening failure mechanism. The appendix restricts the geometric argument more carefully than the broad introductory claim about RGB-only spatial intelligence.
The primary reading route follows the annotated argument: Introduction and mixed-radix interpretation → Uniform and progressive MrRoPE → Analysis and supplementary experiments. The contribution is introduced as a common language for existing context-extension methods, making the subsequent uniform/progressive choices interpretable as parameter changes rather than an unrelated architecture.
The primary reading route follows the annotated argument: Motivation and competing transfer routes → Bridge position, architecture and training → Directional transfer and ablations. The introduction makes the deployment motivation tangible through sensors with different cost and comfort, then narrows the obstacle to adapting pretrained models with limited local training resources.
The primary reading route follows the annotated argument: Analysis of the performance gap → Findings assembled into DepthLM → Depth and multi-task evaluation. The central question asks whether a standard VLM needs a new dense head or regression loss. The paper instead traces failure to input reference and camera scale, making a simple intervention scientifically legible.
The primary reading route follows the annotated argument: An equalizer for musical structure → Construction and uses. The equalizer analogy works because the introduction explicitly separates audible waveform frequencies from latent changes in musical structure. It supplies intuition without treating latent hertz as acoustic pitch.
Mixed contribution: Latent Fourier manipulation defines the representation tool, with listening and interpretation experiments as secondary scientific evidence.
The primary reading route follows the annotated argument: Algebraic setting and encoding → Hierarchical attention and cost → Scaling and ablations. The introduction identifies sequence-length explosion as the practical limitation of prior learned Gröbner-basis solvers. The shape-position restriction and finite-field setting then constrain the ambitious solver framing before architecture is introduced.
The primary reading route follows the annotated argument: Classical formula to neural operator → Operator to complete network → Toy tests to downstream tasks. The introduction uses the analogy between hand-designed filters and CNNs to motivate learning the coefficients of Rodrigues' formula. It immediately names the missing kinematic structure, making the classical-to-neural transition concrete.
The primary reading route follows the annotated argument: Encoder-agnostic feature upsampling → Architecture and crop supervision → Downstream quality and feature preservation. The introduction distinguishes an upsampler shared across encoders from a separate model trained for each encoder. Table 1 makes that distinction inspectable, while cross-encoder results remain empirical rather than proving universal accuracy.
The primary reading route follows the annotated argument: Cost-volume necessity as the question → Background before simplification → Benchmark, transfer and component tests. The central question is unusually crisp: must accurate optical flow explicitly construct a cost volume? The claimed simplification retains recurrent residual updates and a transformer, so the contribution is removal of particular machinery, not absence of inductive design.
The primary reading route follows the annotated argument: Decoder reuse and latent alignment as two bottlenecks → Stitching followed by reward finetuning → Generation and reconstruction evaluation. The introduction identifies separately a weak newly trained 3D decoder and a mismatch between generated latents and decoder inputs. This yields a clean motivation for stitching plus alignment, rather than presenting a disconnected list of techniques.
The primary reading route follows the annotated argument: Reconciling geometry and semantics → Shared encoder, cross-task adapter and staged training → Task results, coupling ablations and failures. The introduction distinguishes externally supplied 3D inputs from geometry inferred from video. This inference-time distinction is useful, but must not be rewritten as absence of geometry supervision: the training objective includes camera, depth and point-cloud targets.
The primary reading route follows the annotated argument: From speech clips to conversations → Continuous tokenization and next-token diffusion → Long-form evaluation and component checks. The introduction motivates full conversations rather than isolated utterances, connecting duration to token rate and speaker consistency. The 90-minute capability claim should remain separate from the systematic benchmark, which spans one to thirty minutes.
The primary reading route follows the annotated argument: Define the task before the architecture → Two failure modes and XFactor → Transfer, probes, ablations and limitations. The paper makes a testable behavioral criterion its central contribution: a pose latent should reproduce the same camera trajectory in another scene. It defines this before introducing the architecture, so the evaluation follows the task rather than a convenient reconstruction metric.
Mixed contribution: The behavioral criterion and metric are important, but the architecture and objective are a distinct deliverable. Resource and empirical tags preserve the mixed argument.
The primary reading route follows the annotated argument: Continuous-token architecture and training recipe → Benchmark results followed by mechanism questions. Use an inset to show local computation inside a simple global architecture, accompany it with a staged training table, and pair appealing galleries with concrete failure examples.
The primary reading route follows the annotated argument: A two-objective modeling gap → One probabilistic identity, three model families → Evaluate observations and latents separately. The introduction frames an explicit tension: a strong marginal density model need not expose useful structured latents, while a latent-variable model need not generate realistic observations. A question links that tension to the proposed family, and numbered contributions distinguish the family, training objective and empirical instantiations. This is more informative than presenting three variants as unrelated inventions.
Mixed contribution: A probabilistic identity, objective and three model families are coupled. Primary representation classification is useful for a drafting route, not exclusive taxonomy.
The primary reading route follows the annotated argument: Define tokenization as a coding problem → Separate allocation from compression → Test reconstruction and overhead separately. The paper turns a practical video-token bottleneck into a precise question about representation length and reconstruction. Contribution bullets then mirror theory, router/compressor design and empirical evaluation. The framing is strongest when near-optimality remains attached to the idealized coding assumptions, rather than implying that the practical lossy tokenizer realizes Shannon-optimal video representation.
Mixed contribution: The practical tokenizer is a representation design; a coding theorem and measured overhead supply secondary theoretical and systems evidence.
The primary change is an objective, update rule, supervision recipe, search/optimization procedure, distillation or adaptation algorithm.
Corpus context · 70 papers: median 5 main figures and 3 main tables; body span ≈ 10.2 pages; attached appendix span ≈ 16.36 pages (69 resolved boundaries). Separate source files: median 3 whole-file pages among 1 papers; these can repeat main text and are not appendix-only lengths. Extraction-assisted observations—not drafting targets.
The annotated methods commonly expose a failure of the existing update or supervision, state an ideal objective, and then distinguish its implementable approximation. Mechanism diagnostics and matched ablations carry a different burden from broad performance comparisons.
Editorial starting package: choose artifacts that test your actual claims. Quantities below are planning suggestions, not counts inferred from all category members or conference rules.
Start with one
Mark teacher/student roles, feedback loops, detached gradients and update targets.
When: Use when information or gradients follow multiple paths.
One explicit objective plus one algorithm when iterative
Expose sampling, stopping, schedules, state, and exact versus approximate steps.
When: A short closed-form update may not need a separate algorithm box.
One or more task-specific tables
Hold backbone/data fixed where the claimed contribution is the training rule.
When: Separate unmatched leaderboard context rather than pooling it with causal comparisons.
One panel per central mechanism claim
Measure the proposed failure, such as gradient conflict, rather than relying on final accuracy.
When: A two-mechanism method may need two different diagnostics, not two decorative plots.
A compact table/curve set
Distinguish component effects, tuning sensitivity, data access and training overhead.
When: Use sensitivity sweeps only for consequential choices.
Optional and conditional
State the assumptions, implementation match and scope of the formal result.
When: Do not transfer an idealized theorem to an amortized or approximate algorithm without justification.
Full derivations, exact loss definitions, pseudocode or implementation references, hyperparameters and tuning budgets, seed-level results, robustness, teacher/data provenance and cost accounting.
Locate the one changed policy-update component while retaining the surrounding estimator; preserve non-wins.
Separate an exact direction from the amortized implementation and give each claimed mechanism an audit.
Use intuition, executable steps and qualified theory as complementary views, not interchangeable proof.
Boundary: No theorem, extra module or longer pseudocode is required just to make an empirical training contribution look theoretical.
See inline examples of these artifact styles · Compare every member in the measurement ledger
The primary reading route follows the annotated argument: Clipping failure and related work → Projection and sparse implementation → Experiments, ablations and appendix. The introduction narrows the intervention to PPO-like clipping while preserving the surrounding estimator and inference behavior, making the claimed novelty unusually easy to locate.
The primary reading route follows the annotated argument: From two-player preferences to populations → Homogeneous formulation and extensions → Evaluation and technical appendix. Use a comparison matrix to make a unifying framework falsifiable: specify which settings recover each baseline. Keep theoretical guarantees attached to the assumptions that support them, especially when introducing a practical extension.
The primary reading route follows the annotated argument: Controlled game and Bayesian formalization → Human study then agent decomposition → Replication and appendix. The game creates a measurable exploration/action tradeoff with a binary answer bottleneck; this is a narrower and testable claim than general rational agency.
The primary reading route follows the annotated argument: Problem formulation and GEPA → Evaluation: numbered observations → Extended applications and appendix. The title says the method can outperform RL, while the formulation explicitly permits prompt-space versus weight-space optimization; this helps readers understand what is actually being compared.
The primary reading route follows the annotated argument: Introduction and related work → SparseRL → Experiments, ablations, and discussion → Appendix A. The opening two-kernel example grounds the problem in different correct implementations with different runtimes. This motivates a hardware-aware objective more concretely than a generic claim about code-generation capability.
The primary reading route follows the annotated argument: Background and common matching loss → Universal inverse distillation and real data → Unified experiments → Theoretical and experimental appendices. Two explicit gaps—fragmented distillation formulations and adversarial machinery for incorporating real data—map directly onto the two contributions. The background establishes shared notation before claiming universality.
The primary reading route follows the annotated argument: A concrete stagnation failure → Formal QD and algorithm lineage → Method, new setting, controlled comparisons. The hiker example distinguishes optimizing one image from seeking a diverse archive, making the unusual optimization objective understandable.
The primary reading route follows the annotated argument: Three criteria for graph ICL → Fingerprints, alignment and episodic matching → Node transfer, edge transfer and ablations. A compact criteria table establishes which combination is claimed new before introducing the method.
The primary reading route follows the annotated argument: Motivation and in-batch language modeling → Benchmarking followed by controlled analysis. The introduction makes the missing supervision problem concrete before proposing next-token prediction as the training signal, rather than introducing another retrieval loss without motivation.
The primary reading route follows the annotated argument: Projection rather than unconstrained generation → Representation, bidirectional search and holistic edits. The introduction explains why heuristic synthesizability scores are insufficient and why projection can complement an existing molecular optimizer, giving the contribution a practical interface.
The primary reading route follows the annotated argument: Privacy challenge to three design components → Utility, attacks and deployment variants. The introduction motivates adaptive noise using distinctive versus common trajectories, making the application-level concern concrete before introducing DP notation.
The primary reading route follows the annotated argument: Three principles and three computation steps → Performance, scale and specialization. The problem is defined as a mismatch between router decisions and expert responses, not simply a demand for more expert diversity. Three design principles constrain the proposed fix.
The primary reading route follows the annotated argument: Motivation and failure → Algorithm, guarantees, evidence. The introduction connects hypothesis-driven trials to exploratory discovery using a concrete ant-behaviour example rather than an abstract foundation-model promise.
The primary reading route follows the annotated argument: Practical evaluation before model design → Cold start, preference alignment, online exploration. The introduction identifies a protocol mismatch, not merely an accuracy deficit: single-source training underrepresents realistic mixed-source deployment.
The primary reading route follows the annotated argument: Evaluation, data, model → Shared reward objective, distinct model families. Modality imbalance and fixed implicit preferences are introduced as separate obstacles; free-form criteria are part of the formal sample tuple, making the second obstacle operational.
Mixed contribution: Data, benchmark and two reward-model families are all named contributions. Primary learning/optimization is a browsing choice, not an exclusive account of the paper.
The primary reading route follows the annotated argument: Estimator design space → RLR and diffusion experiments → Theory and implementation appendix. The opening compares full differentiation, truncated differentiation and policy gradients as competing resource tradeoffs; this gives the hybrid design an intelligible motivation before its equations.
The primary reading route follows the annotated argument: Two measurable desiderata → Theory to a short iteration → Three learning regimes. The paper explicitly distinguishes continual adaptation with old data available from restricted-memory catastrophic-forgetting settings. This scope statement prevents an otherwise ambiguous stability claim.
The primary reading route follows the annotated argument: Train–test mismatch → Closed-loop error recycling → Generation regimes and stress tests. The introduction reframes drifting as a training–inference mismatch and contrasts generation with restoration. This motivates training on errors rather than adding another inference-time heuristic.
The primary reading route follows the annotated argument: A different reasoning medium → Initialization then reinforcement learning → Performance, failure analysis and generalization. The first comparison diagram makes the proposed change of representation explicit: actions emerge from image-state transitions instead of being verbalized. It frames visual planning as a supplement, not a universal replacement for language.
The primary reading route follows the annotated argument: Two bottlenecks and space–time separation → Constructing and solving the reduced system → Experiments organized by PDE difficulty. The introduction names optimization difficulty and non-causal time treatment separately, then maps the proposed solver components to those obstacles.
The primary reading route follows the annotated argument: Move reinforcement to the forward process → Positive/negative policy geometry and implicit objective → Multi-reward results, head-to-head speed and stability ablations. A single question about forward versus reverse optimization gives the paper a clear organizing contrast instead of an undifferentiated list of engineering improvements.
The primary reading route follows the annotated argument: Offline MOO as conditional sampling → Benchmark breadth, component ablations and limitations. The introduction states a concrete tension: improve beyond a fixed dataset without exploiting unreliable surrogate predictions. This motivates direct conditioning and gives each method component a clear purpose.
The primary reading route follows the annotated argument: Introduction → Preliminaries / Method → Experiments → Appendices A–I. The opening contrasts bootstrapped offline RL with conditional trajectory imitation, then identifies missing quality feedback and unprincipled extrapolation as distinct problems. Contributions answer those problems in the same order.
The primary reading route follows the annotated argument: Introduction / Preliminaries / Rethinking Length Rewards → Decoupled Rewards and Curriculum Data Scheduling → Experiments / Analysis → Appendices A–I. Two failure modes—penalizing essential reasoning and rewarding redundant continuations—organize both the analysis and method. The problem is credit assignment inside a sequence, not an assertion that all long reasoning is undesirable.
The primary reading route follows the annotated argument: Introduction / Preliminaries → Mean velocity policy / Instantaneous constraint → Mean flow RL / Experiments → Appendices A–F. The opening converts a practical control bottleneck into a specific question: whether expressive action distributions require repeated numerical integration. The proposed answer concerns a mean-velocity policy, rather than treating all generative policies as inherently slow.
The primary reading route follows the annotated argument: Introduction / Self-improving as tree-search → Huxley–Gödel Machine → Experiments → Appendices A–F. The paper gives a precise name to the gap between current benchmark performance and future self-improvement potential. It then defines separate expansion, evaluation and scheduling decisions, making the proposed optimization target easier to distinguish from task accuracy.
The primary reading route follows the annotated argument: Current methods do not benefit from long sequence input → Method → Experiments and extended analyses. The introduction challenges context length with two complementary diagnostics: retraining at different lengths and truncating a long-trained model at test time. That distinction strengthens the motivation beyond a single favorable short-input comparison.
The primary reading route follows the annotated argument: Motivation and theoretical insights → Capacity Manipulation → Experiments and further analysis. The opening diagnostic is specific: similar raw diffusion losses coexist with minority sensitivity to removing small-magnitude parameters. This motivates a capacity-allocation hypothesis without making loss imbalance the sole explanation.
The primary reading route follows the annotated argument: Fingerprint paradigm and instantiation → Effectiveness, robustness and stealth → Detector, ablations and examples. The introduction identifies two independent weaknesses in query–key fingerprints, then replaces both with a semantic query domain and a distributed statistical signal. That parallel problem–solution structure makes the novelty easy to locate.
The primary reading route follows the annotated argument: Failure mechanism to operational proxy → Outcomes and mechanism diagnostics. A repeated-question failure becomes a named mechanism before a method is proposed, giving the reader a causal narrative rather than a collection of optimization tricks.
The primary reading route follows the annotated argument: Empirical stability motivates a decomposition theorem → Complementary decoders and practical validation. An observable inefficiency—many route edges do not change—provides a concrete motivation before architecture details.
The primary reading route follows the annotated argument: Data difficulty as the bottleneck → Generalization, preservation and attribution. The introduction turns a vague need for better reasoning into three obstacles: task difficulty, expensive long rollouts and short-task retention.
The primary reading route follows the annotated argument: A two-level corruption taxonomy → Training robustness and inference reasoning. Concrete mismatches explain why noisy correspondence differs from merely missing modalities; a manual sample audit supports the practical motivation.
The primary reading route follows the annotated argument: Offline limitation → online task inference → Preliminaries → regret analysis → practical method → Question-led experiments. The opening contrasts collecting labels for an existing dataset with actively requesting rewards during deployment; this identifies the changed information-access assumption, not merely an improved return.
The primary reading route follows the annotated argument: Unobserved-direction failure → physical consistency → Principle → ray tracing → two-stage framework → Quality → ablation → adaptation cost. The gap is precise: radiance trained only from camera views remains unconstrained along directions needed for indirect illumination. Related work differentiates cached radiance, learnable residual light and differentiable tracing.
The primary reading route follows the annotated argument: Training-cost motivation → measured search state → Sense → reason → act → Solver benchmarks → component and frequency ablations. The method deliberately leaves the numerical optimizer in place and uses language models as hyperparameter supervisors; this distinguishes the work from generating candidate solutions or new operators.
The primary reading route follows the annotated argument: Two personalization failures → evaluation chains → Three training stages → two scaling levels → Preference accuracy → prototypes → OOD and policy evaluation. The introduction distinguishes scenario-dependent preferences from sparse-history generalization, allowing persona-conditioned rubrics and prototype transfer to answer different questions.
The primary reading route follows the annotated argument: Scarce comparisons to thresholded unpaired responses → Toy validation, real-data results, and mechanism checks. The introduction identifies an available but structurally incompatible resource—single SFT responses—and explains why pairwise preference methods cannot directly consume it.
The primary reading route follows the annotated argument: Three barriers and matching design choices → Three experimental questions. The introduction and preliminary desiderata use the same three-part organization, making each proposed component answer a named limitation.
The primary reading route follows the annotated argument: Introduction, related work, preliminaries → Methodology → Experiments and appendices. Two claimed gaps—token-importance estimation and coarse preference supervision—map directly onto the two proposed components.
The primary reading route follows the annotated argument: Introduction and related work → Generative formulation and EM procedure → Question-led experiments and appendices. Figure 1 gives one concrete image–text example before contrasting reweighting, realignment, and many-to-many correspondence generation. The two mismatch definitions then formalize exactly what those illustrations are meant to solve.
The primary reading route follows the annotated argument: Three representation desiderata and five theoretical questions → Practical objective and proposition-led experiments. Table 1 translates the familiar information-bottleneck formulation into a dynamical prediction setting before introducing additional Koopman constraints.
Mixed contribution: A practical representation objective is derived from propositions; this is theory-guided learning rather than a theory-only contribution.
The primary reading route follows the annotated argument: Bridge two vocabularies → Derive before specializing → Separate guarantees and empirical questions. The introduction locates the gap along three independent axes: single-step prediction, single-policy learning and dependence on on-policy data. It then names the components that address them. This is a useful way to explain a combination of established ideas without pretending each component is new.
The primary reading route follows the annotated argument: Two optimization failures → Vector objective and implementation → Tasks, ablations and limitations. The introduction gives two distinct bottlenecks memorable names, then reuses them in the method and gradient diagnostics. This makes the proposed optimization change easier to locate than a generic compression motivation alone.
The primary reading route follows the annotated argument: Parameter-space observation followed by its limitation → Two-stage method and research-question-led evaluation. The motivating failure is concrete: a correct reasoning answer can still violate a requested format. The paper distinguishes parameter compatibility from behavioral compatibility.
The primary reading route follows the annotated argument: Hard constraint to optimal policy to transformed pair data → Safety, helpfulness and over-refusal as separate outcomes. The introduction makes reduced training complexity concrete by contrasting auxiliary reward/cost networks with a transformed preference dataset.
The primary reading route follows the annotated argument: Introduction and preliminaries → Method and experiments → Appendices A–J. Two clearly named imbalance axes create a testable unification claim; graph-level classification is distinguished from node- and edge-level tasks.
The primary reading route follows the annotated argument: Preliminary and AgentFlow → Flow-GRPO and experiments → Algorithm, theory and operational appendix. Make training ownership, runtime collaboration and evaluation access separately visible, and pair behavioral claims with trace-level evidence that survives checking.
The primary reading route follows the annotated argument: Methods → Experiments and ablations → Appendices. The contrast is explicit: train state/action embedders while leaving the answer-generating LLM frozen. The main text honestly states that reported rewards use supporting facts, not the proposed future LLM-feedback alternative.
The primary reading route follows the annotated argument: Bottlenecks and dual consistency → Temporal geometry and applications. The opening decomposes runtime before proposing acceleration, making the two consistency targets traceable to measured bottlenecks rather than a generic compression claim.
The primary reading route follows the annotated argument: Three explicit research questions → Complementary evaluation and case studies. Use research-question headings and matched learning curves to connect design choices with evidence, and publish evaluation prompts plus specific observed failure modes.
The primary reading route follows the annotated argument: Scheduling problem and algebraic bridge → Main comparison and factor analysis → Implementation and broader controls. The initial comparison diagram makes the operational difference visible: keep the live training rate stable and form evaluation models from stored checkpoints. Scheduling flexibility is distinguished from downstream performance.
The primary reading route follows the annotated argument: Explain why likelihood accuracy changes the task → Separate the mathematical target from the finite model → Make cost and quality auditable. The paper motivates a specific bottleneck: a molecular proposal must have sufficiently accurate likelihoods for importance reweighting, not merely produce plausible samples. Its contribution bullets separate the objective, architecture, evaluation cost and sample quality. Table 1 compares model families against explicit requirements, making the gap more concrete than a generic faster-generator claim.
The primary reading route follows the annotated argument: Task-free motivation and formalization → Attention-based evolutionary operators → Four research questions. The opening comparison distinguishes learning from a task distribution from learning during a single optimization run. The phrase task-free is defined operationally, not as absence of a target task.
The primary reading route follows the annotated argument: Two representation deficiencies → Local/global extraction, compactness and consistency → Layered empirical audit. The two stated deficiencies map directly to the local/global views and compactness module, making the method’s organization predictable from the introduction.
The primary reading route follows the annotated argument: EMA as online regression → Factorized moments → Pretraining, fine-tuning and rank ablations. The central equivalence is stated as one short equation before the full method. This creates an accessible entry point into optimizer-state compression without beginning with several pages of matrix calculus.
The primary reading route follows the annotated argument: Two generation paradigms and their opposing defects → Dual-mode pretraining → cross-mode distillation → OOD co-training. The early comparison grid gives names and visible failure modes to the multi-view-oriented and 3D-oriented alternatives before explaining why naive distillation is insufficient.
The primary reading route follows the annotated argument: Compositional motivation → concept extraction → relational distillation → Matched baseline pairs → component controls → visual mechanism. For an add-on method, present matched with/without pairs across host algorithms, isolate capacity and representation-use controls, and disclose both reusable preparation cost and per-host overhead.
The primary reading route follows the annotated argument: Memory bottlenecks and decomposition precedents → Stable subspaces and joint iteration → Hypothesis tests, tradeoffs and devices. The introduction distinguishes reducing trainable parameters from reducing saved activations or inference work. This taxonomy clarifies the specific gap claimed for joint weight-activation compression.
The primary reading route follows the annotated argument: Application-specific numerical requirements → Scalar minimax reduction and greedy construction → Finite precision and measured optimizer effects. The paper motivates a numerical-linear-algebra contribution through throughput and precision requirements, explicitly holding Muon's update rule fixed. This keeps the algorithmic claim narrower than inventing a new optimizer.
Mixed contribution: A numerical algorithm improves an existing optimizer and has an explicit optimality class. Its theorem and systems performance must remain attached to the algorithmic claim.
The primary reading route follows the annotated argument: Planner-aware likelihood and its practical surrogate → Experiments and appendix. The boxed question names a precise mismatch: training with uniform unmasking while sampling with a confidence-based planner. This is more informative than claiming diffusion language models are generally inefficient.
The primary reading route follows the annotated argument: Observed transient instance cues → IEP, ABDiv, distillation and propagation → Benefits and domain failures. Two visible errors—merging adjacent objects and fragmenting single objects—organize the motivation. The proposed boundary cue answers those errors directly rather than treating generative features as generally superior representations.
The primary reading route follows the annotated argument: Three research questions → Atomic verification capabilities → Sequential generation and editing. The introduction's three questions map directly to benchmark, training and application sections. This gives a multi-contribution paper a coherent dependency chain: establish a verification gap, train a verifier, then test whether its feedback improves generation.
Mixed contribution: Benchmark diagnosis, verifier training and application are three linked contributions; learning/optimization is the chosen primary, not a statement that the benchmark is unimportant.
The primary reading route follows the annotated argument: Contrastive observation and data construction → Think, contrast and rethink → Baseline comparisons and design questions. A skateboard example motivates the narrower question of repairing visually ungrounded rationales. The opening observation is concrete, but correct final answers and selected examples do not alone verify every intermediate visual claim.
The primary reading route follows the annotated argument: Long-document bottleneck and memory workflow → Multi-conversation outcome-reward optimization → Length sweeps, ablations and failure traces. The central distinction is processing millions of input tokens through repeated bounded-context calls, not creating a million-token attention window. The 1,024-token memory is overwritten after each 5,000-token chunk; the question remains available throughout processing.
The primary reading route follows the annotated argument: Three obstacles to emotional explanation → Prosody supervision and progressive rewards → Recognition and explanation evaluation. The three-challenge introduction provides a direct outline for dataset construction, prosody-centric SFT and reward design. However, plausible explanations of an emotion label are not necessarily causal explanations of the model's actual decision.
The primary reading route follows the annotated argument: Reverse a short self-training update → Local gradient geometry → Families, trade-offs and implementation details. The paper turns self-training degradation into a constructive question: can reversing the update help? The one-line merge keeps the intervention memorable while distinguishing training-time adaptation from extra inference-time guidance or model compression.
The primary reading route follows the annotated argument: Exploration bottleneck and two-stage method → Theory, practical algorithms and research questions → Proofs and reproducibility supplement. The opening gives diffusion two distinct jobs: model heterogeneous replay data and provide a reusable policy prior. Gaussian acting is then motivated by interaction cost rather than presented as an unrelated component.
The primary reading route follows the annotated argument: Coverage is sufficient but not necessary → Parameterized empirical optimization and theory → Calibration and experiments. The introduction contrasts the desired event—loss below a certificate—with the stronger event that the outcome lies in a prediction set. This precise sufficient-versus-necessary distinction supplies the entire motivation.
The primary reading route follows the annotated argument: A new inference task before a method → Two inductive biases and multiple demonstrations. Concrete reward-weight and quantile examples explain why the surrogate predicts output distributions rather than a scalar Bayesian-optimization objective; notation links the user problem to trajectory inference.
The main deliverable changes inference, decoding, execution, compilation, memory movement or resource use, including a runtime algorithm whose correctness/quality must be preserved.
Corpus context · 23 papers: median 4 main figures and 3 main tables; body span ≈ 10.17 pages; attached appendix span ≈ 11.89 pages (21 resolved boundaries). Separate source files: median 26.5 whole-file pages among 2 papers; these can repeat main text and are not appendix-only lengths. Extraction-assisted observations—not drafting targets.
The clearest studies measure a bottleneck first, show the changed execution or information flow, and distinguish theoretical work reduction from end-to-end latency. They report quality beside the resource denominator and reveal hardware, precision and approximation choices.
Editorial starting package: choose artifacts that test your actual claims. Quantities below are planning suggestions, not counts inferred from all category members or conference rules.
One compact breakdown or trace
Show which computation, memory movement or latency dominates before claiming improvement.
When: The denominator must represent the workload that the system aims to improve.
One overview with a running example
Mark cache, parallel workers, synchronization, retained/dropped tokens or changed instructions.
When: Choose a diagram or code view based on the mechanism; do not add both unless they answer different questions.
One trade-off panel or small-multiple family
Pair quality with measured latency, throughput or memory over comparable workloads.
When: Label hardware, batch size, precision and budget; distinguish iso-batch and maximum-capacity comparisons.
A table or breakdown plus scaling curve when claiming scale
Show that the isolated speedup survives the full pipeline and identify overhead.
When: Strong/weak scaling are different tests; use the one supported by the claim.
As needed by the algorithm
Expose acceptance, fallback, cache invalidation or lossless distribution constraints.
When: A claimed lossless sampler needs a precise distributional contract, not only matching benchmark scores.
Hardware/software versions and commits, workload shapes, tuning budgets, kernels, exact baseline flags, memory definitions, warm-up/timing protocol, correctness tests and non-favorable regimes.
One running code example bridges syntax and compilation; small multiples keep speed and code complexity visible.
Connect fused kernels to the memory hierarchy, then place accuracy beside the scale/cost evidence.
Separate token retention, frame capacity and measured runtime; representative selection and residual merging have different jobs.
Boundary: Fewer tokens/FLOPs/steps are not automatically the same speedup in wall time; a fast subroutine does not establish a faster complete application.
See inline examples of these artifact styles · Compare every member in the measurement ledger
The primary reading route follows the annotated argument: Two formal problems → Kernels, guarantees and implementation → Applications and experiments. Numbered Problem 1.1 and Problem 1.2 state the decision and computational budget explicitly, preventing angle estimation and angle testing from being conflated.
The primary reading route follows the annotated argument: Reframe the abstraction → Notation, implementation and worked comparisons. The opening contrasts a reduction call with explicit loops before naming the abstraction, giving readers a familiar problem rather than beginning with new syntax.
The primary reading route follows the annotated argument: Programming model → Scheduling and evaluation. The introduction presents performance and implementation complexity as a two-objective tension, then keeps both metrics visible throughout evaluation.
The primary reading route follows the annotated argument: Bottlenecks before kernels → Single-device to distributed design → Feasibility, accuracy and implementation. The introduction translates acquisition resolution into transformation-parameter and memory requirements, making the engineering gap tangible without introducing an unnecessary new learning model.
The primary reading route follows the annotated argument: Tokenwise recovery as a baseline → Naive hierarchy then capped hierarchy → Proofs, examples and implementation. Joint intractability is named as the specific bottleneck, which distinguishes a verifier contribution from another draft-model improvement.
The primary reading route follows the annotated argument: Executable code versus statistical reliability → Constrained fragments and diagnostic refinement → Diagnostic comparisons and failure taxonomy. A deprecated distribution argument gives a concrete entry point, while the text distinguishes programming semantics from natural-language meaning.
The primary reading route follows the annotated argument: Introduction / Prior work → Parallel application / Limitations / Cell adaptations → Implementation / Results → Appendices A–E. The introduction explicitly credits earlier Newton-based parallelization and defines the new contribution as scalable kernels, seven-billion-parameter experiments and a reusable library. This is a persuasive systems claim without requiring a newly invented mathematical primitive.
The primary reading route follows the annotated argument: Motivation: efficient transitions for reward alignment → GLASS transitions, denoiser and velocity → Posterior sampling, sampling schemes and reward alignment. The contribution targets transitions used inside alignment algorithms, not another reward objective. This positioning explains compatibility with SMC, search and guidance before introducing the sufficient-statistic derivation.
The primary reading route follows the annotated argument: Motion-controlled teacher → Causal distillation → Experiments and streaming demo. Latency, causality and bounded duration are introduced as separate obstacles to interactive video. That framing supports a system contribution with several coordinated changes rather than pretending all gains arise from one loss term.
The primary reading route follows the annotated argument: Saliency observation → SGRS and LocoRE → Benchmark and diagnostic evidence. The observation-to-intervention narrative is easy to follow, but the early claim of a direct causal link is stronger than the documented evidence. The conclusion's correlation wording and appendix's high-saliency failures are more appropriately bounded.
The primary reading route follows the annotated argument: Locality observation and locality-aware decoding → Decoupled queries, experiments, and implementation appendices. The paper distinguishes parallel prediction from merely selecting an order: query tokens expose target locations without pretending that simultaneously sampled image tokens are already known.
The primary reading route follows the annotated argument: Equivalent formulations to an implementable solver → Runtime, success, and extensions. The introduction identifies a precise gap: knowing the optimal objective value does not supply an efficient optimal transport sampler. This is more informative than a generic claim that prior decoding is slow.
The primary reading route follows the annotated argument: Observation before intervention → Performance, composition, and scope. The central change is when to terminate refinement, not how to cache attention or train a smaller model. The related-work taxonomy makes those complementary intervention points explicit.
The primary reading route follows the annotated argument: Mode-averaging problem → Compositional diffusion with guided search → Robotics and long-content experiments. The running two-route example shows why individually plausible local modes can produce an invalid global sequence, giving the method a concrete target.
The primary reading route follows the annotated argument: Capability question and target distribution → Power distribution, MH and blockwise sampler → Performance, likelihood and diversity. The introduction asks whether existing capabilities can be elicited by sampling rather than whether RL creates capabilities. This narrower intervention makes the experiments interpretable without resolving the entire philosophical debate.
The primary reading route follows the annotated argument: Can environment interaction be parallelized? → Instantiations and tradeoffs. The paper shifts the unit of speculation from tokens to environment API calls. A direct research question makes that abstraction change easy to understand.
The primary reading route follows the annotated argument: Motivating analyses → ThinKV and Continuous Thinking → Evaluation and supplementary details. The introduction narrows the problem to long generated reasoning, distinguishing it from prompt-cache compression. Four contributions map directly to observation, algorithm and systems sections.
The primary reading route follows the annotated argument: Observation before mechanism → Complementary modules → Accuracy, capacity and runtime. The motivation identifies a concrete failed assumption—semantic correspondence need not remain at the same image coordinate. That observation supports the tree-merging design more persuasively than a general statement that videos contain redundant tokens.
The primary reading route follows the annotated argument: Unifying negative guidance → Safety, diversity and memorization experiments. The introduction identifies a precise unresolved question—when to intervene—rather than simply proposing stronger suppression. The three contribution bullets distinguish formulation, equivalence claims and timing analysis.
The primary reading route follows the annotated argument: Provenance problem → invertible embedding pipeline → Theory → fidelity, tracing, speed → ablations. Give each advertised property its own evaluation axis and disclose what is excluded from speed measurements directly beside the figure.
The primary reading route follows the annotated argument: Heterogeneous feature dynamics → Offline selection and inference → Task-wise results and compatibility. The introduction identifies an overlooked unit of heterogeneity: feature dimensions, not tokens. The trajectory plots motivate assigning different predictors instead of only increasing a single predictor's order.
The primary reading route follows the annotated argument: Sampling problem and collision-probability threshold → Temperature sweeps, efficiency and diversity → Proofs, protocols and generated examples. The title's hyperparameter-free claim concerns the truncation threshold, computed as the sum of squared token probabilities. Temperature still changes the distribution and remains an explicit experimental and deployment choice.
The primary reading route follows the annotated argument: Computational bottleneck and differentiation primer → Structure-exploiting GPU solver → Microbenchmarks, learning and vehicle case study. The introduction names a hardware-algorithm mismatch rather than claiming a generic faster optimizer. Related work separates general-purpose GPU solvers from structure-aware MPC solvers, locating the contribution at their intersection.
The primary contribution is a theorem, characterization, bound, formal equivalence, impossibility result or mathematical explanation under explicit assumptions.
Corpus context · 33 papers: median 3 main figures and 1 main tables; body span ≈ 10.11 pages; attached appendix span ≈ 21.39 pages (32 resolved boundaries). Separate source files: median 10 whole-file pages among 1 papers; these can repeat main text and are not appendix-only lengths. Extraction-assisted observations—not drafting targets.
Useful examples introduce a concrete object or counterexample before dense notation, organize intermediate results around obstacles, and link empirical diagnostics to the specific theoretical ingredients. Assumptions and practical departures remain visible.
Editorial starting package: choose artifacts that test your actual claims. Quantities below are planning suggestions, not counts inferred from all category members or conference rules.
Start with one when the object is unfamiliar
Make definitions operational and reuse the same example through the proof.
When: A clean symbolic example may be better than a decorative architecture diagram.
One table or short roadmap
Distinguish settings, quantifiers, rates, regimes and what each theorem contributes.
When: Especially useful when several theorems have different assumptions.
As required by the actual claims
Expose the central logical step without concealing necessary conditions in the appendix.
When: No target number of theorems; redundant lemmas are not evidence of significance.
Only for an empirical/theory-illustration claim
Test a predicted scaling, regime change or ingredient under a stated experimental model.
When: Finite experiments illustrate or test consequences; they do not prove an asymptotic theorem.
Include where it clarifies scope
Show which tempting generalization is unsupported and why assumptions matter.
When: Not every formal paper needs a benchmark leaderboard.
Section-matched proofs, notation index, technical lemmas, explicit constructions, numerical protocols and differences between theorem model and implemented experiment.
Reuse a counter-machine example while keeping ideal attention/activation assumptions distinct from trained finite models.
Keep the two analytical routes separate and align normality diagnostics with the quantities each route predicts.
Tie a graph abstraction to observed sample groups, then match each intervention to its own controlled comparison.
Boundary: An empirical diagnostic, failure to reject a test, or comparison of upper bounds does not prove a stronger universal claim about actual models.
See inline examples of these artifact styles · Compare every member in the measurement ledger
The primary reading route follows the annotated argument: Geometric motivation and background → Gaussian case then general distributions → Experiments and discussion. The introduction distinguishes minimizing projection residuals from maximizing projected variance; this prevents a familiar PCA name from hiding a changed mathematical objective.
Mixed contribution: A geometric algorithm is proposed, but the annotated argument is built around a special-case construction and its mathematical extension; learning/optimization remains secondary.
The primary reading route follows the annotated argument: Definitions and staged expressivity results → Construction, experiments and technical appendices. The introduction contrasts soft attention with earlier hard-attention constructions, then states the additional chain-of-thought and positional assumptions rather than presenting a generic capability benchmark.
The primary reading route follows the annotated argument: Geometric vocabulary → Bounds and construction. The gap is not how many linear regions exist but how they connect, sharply separating this contribution from region-counting literature.
The primary reading route follows the annotated argument: Why existing assumptions fail → Formal guarantee and validation. The introduction identifies a precise proportional-dimensional failure of standard assumptions rather than broadly claiming prior unlearning methods do not work.
The primary reading route follows the annotated argument: Question and two routes → Diagnostics. The introduction converts a familiar alignment/uniformity story into a precise distributional question and explains why closed-form Gaussian quantities would be useful.
The primary reading route follows the annotated argument: Intuition before formalization → Controlled model ladder → Limits and extensions. The paper isolates memory duration as a capacity constraint, distinguishing when a feature occurred from what it represents before introducing algebra.
Mixed contribution: The controlled RNN ladder mixes mathematical explanation and empirical geometry. Primary theory is editorial and should be read with the empirical-analysis tag.
The primary reading route follows the annotated argument: Motivation, related work and problem setting → Theoretical analysis → Experimental results and appendices. The introduction poses a selection question rather than another generator proposal; its explicit classwise abstraction makes the theory-to-practice gap visible early.
The primary reading route follows the annotated argument: Introduction / The model → Methodological overview → Results / Conclusion and discussion → Appendices A–F. The broad optimization motivation narrows to curvature at random initialization, not a theorem about entire training trajectories. Defining the sample ratio, widths and normalized loss prevents an informal claim about all overparameterized networks.
The primary reading route follows the annotated argument: Introduction / Background → Methods → Comparative analysis / Experiments → Appendices A–G. Figure 1 demonstrates the central distinction before dense geometry: eliminate rotational wandering while preserving the endpoint distribution. Table 1 then separates learning redundancy from sampler compatibility, making the comparison criteria explicit.
Mixed contribution: The argument derives geometric projection/lifting results and then a usable molecular method. Both theoretical and methodological reading routes are relevant.
The primary reading route follows the annotated argument: Introduction / Related Work / Preliminaries → Private Set Union → Applying the Weighted Gaussian Mechanism → Experiments / Future Directions / Appendices. The contribution is a different utility lens and guarantees for a simple existing mechanism, not a needlessly renamed new algorithm. Frequency-weighted missing mass explains why recovering fewer unique items can still preserve useful data mass.
The primary reading route follows the annotated argument: Assumptions, deductions and observations → Optimal context length → Proof of concept with synthetic data. The paper begins with apparently conflicting long-context observations, then uses Bayes risk plus approximation error to organize them. The question is explanatory, not a claim to introduce a new context-extension architecture.
Mixed contribution: Loss-decomposition deductions and empirical proxies are coupled. The theory label does not imply that every real-data interpretation is proved.
The primary reading route follows the annotated argument: Background → Non-asymptotic analyses → Explicit bounds and auxiliary tools. The gap is carefully about finite-confidence analysis of established algorithms, not introducing a new bandit solver. Related work distinguishes modifying the algorithm to stabilize sampling from analyzing its original behavior.
The primary reading route follows the annotated argument: Long-form generation and GSSMs → Impossibility and interactive construction → Synthetic and coding experiments. The paper turns an architectural limitation into a systems question: fixed internal memory is not fixed total memory when a model can repeatedly read and write an external store. That boundary change is the essential contribution, not merely adding a longer prompt.
The primary reading route follows the annotated argument: Two geometrical failures motivate spacetime → Tractable estimation, applications and limits. The introduction establishes a precise obstruction instead of announcing a new metric without explaining why familiar constructions fail.
The primary reading route follows the annotated argument: Four feedback regimes and hardness → Utility estimation → no-regret wrappers → equilibria → Routing illustration and proof appendices. The motivation asks what information numerical rewards provide that ordinal rankings lose. It carefully distinguishes instantaneous rankings from rankings of accumulated experience.
The primary reading route follows the annotated argument: Observable phenomenon → architectural ablation → Simplified model → construction → lower bound → Beyond Markov. Randomly drawing a new transition matrix for each sequence gives an identifiable in-context task: memorizing one training chain cannot solve the held-out chain.
The primary reading route follows the annotated argument: Representer theorem, constructive approximation, and complexity → Controlled processes and real event data. The introduction explains why integrated, history-dependent point-process losses break the classical representer argument before specifying the linear least-squares regime that restores tractability.
The primary reading route follows the annotated argument: Introduction and preliminaries → Equivalence characterization and learning → Appendices A–D. The introduction uses three explicit questions to connect the identifiability gap, equivalence criteria, and learning algorithm. Familiar CPDAG and covered-edge-reversal analogies make the new setting legible without equating the two theories.
The primary reading route follows the annotated argument: Concrete mediation example, explicit information model and optimal policies → LLM simulation as a comparison with analytical behavior. The restaurant example explains why an informative message affects both the current decision and future observers. This makes an information externality understandable before introducing value functions.
The primary reading route follows the annotated argument: Semantic association as the question → Theorem and interpretation → Controlled and practical-model experiments. Concrete token relationships introduce the abstract target, while the research question is isolated before notation. This helps readers understand why a matrix characterization is useful.
The primary reading route follows the annotated argument: Observation → controlled example → graph model → Removal, margin, temperature. The opening moves from coreset observations to deliberately mixed CIFAR examples, giving the theoretical question an empirical entry point.
The primary reading route follows the annotated argument: Exchangeability theory → GraphHash and experiments. The object of symmetry is embedding coordinates across randomly initialized and trained networks, not graph nodes. Defining this probability space early prevents readers from confusing exchangeability with independent features.
The primary reading route follows the annotated argument: Succinctness versus expressive capacity → Verification complexity and representation gaps. The opening resolves an apparent contradiction: a recognizer can express fewer languages yet describe some shared languages much more compactly. This cleanly separates the research question from raw expressive power.
The primary reading route follows the annotated argument: Five takeaways and theorem maps → Planner/predictor idealization → Simulations and implications. An arithmetic-expression example introduces parallel subproblems before complexity classes. Five numbered takeaways then provide a reading route, each linked to its theorem rather than functioning as an unsupported claim list.
The primary reading route follows the annotated argument: Anisotropy changes the learning trajectory → Empirical phases and theorem roadmap → Proof outline and complete appendices. The paper separates prediction loss from sign-invariant parameter recovery MSE. That choice matters: high-variance directions can give good prediction while weak spectral directions remain unrecovered, explaining why the two convergence stories differ.
The primary reading route follows the annotated argument: Cost-aware model and inexact composite-gradient framework → SAGA/SVRG variance bounds and recursive correction → Experiments and extensive appendix. The motivating distinction is costly arbitrary client selection versus cheaper random selection and a reliable delegate, not simply fewer communication rounds. This makes the resource model part of the contribution.
The primary reading route follows the annotated argument: Natural geometric signature → Forgery resistance and extraction cost → Authentication analogy and limitations. Four named properties organize the contribution and distinguish its niche from watermarks. An early footnote narrows every model to architectures with a final normalization layer, and a generation step means a complete next-token probability vector, not one generated word.
Mixed contribution: A geometric signature is derived and used to propose authentication. The formal property is not evidence of a complete deployed security system.
The primary reading route follows the annotated argument: Counterexample to cross-entropy as downstream predictor → Coverage generalization and optimizer interventions → Proof roadmap and experimental construction. A graph-reasoning example shows why improving likelihood need not improve best-of-N performance. Coverage is then defined as a density-ratio tail probability: smaller is better. This definition prevents conflation with the everyday meaning of coverage or with Pass@N itself.
The primary reading route follows the annotated argument: Constrained transport and coding → One-shot and asymptotic regimes → Neural implementation and empirical trade-offs. The motivation distinguishes restoring a target distribution from reproducing a degraded input. However, the distortion remains between input and reconstruction, so the reader must keep distributional restoration separate from paired clean-target error.
The primary reading route follows the annotated argument: Event processes to window graphs → Observed parents to latent surrogates → Alternating discovery and sensitivity tests. The introduction distinguishes entirely unobserved event streams from missing timestamps within known streams. This precise gap explains why the method must discover the number and connections of latent subprocesses rather than merely impute events.
The primary reading route follows the annotated argument: An ideal equivalence meets restricted policies → Counterexample, local geometry, repair. The contribution targets a specific gap between the unconstrained DPO derivation and a restricted policy class. Population-data counterexamples separate misspecification from finite-sample or optimizer failure.
Mixed contribution: Restricted-policy misspecification is established before the repair; the theory label does not make the proposed correction incidental.
The primary reading route follows the annotated argument: Precision and error regimes → Simulation lemmas and qualitative experiments. The introduction identifies an exact gap between eventual identifiability and quantitative training-length bounds, then states agreement of two limit transformers as the actual object of study.
The primary reading route follows the annotated argument: Two model reductions and contributions → Rates, spectra and interpretation → Conjecture, numerical checks and derivations. The introduction narrows the broad scaling-law problem to two solvable architectures with Gaussian inputs and power-law target weights. The reductions make the scope concrete before the phase diagram appears.
The main new knowledge is a measured phenomenon, explanation, controlled comparison, failure analysis or audit; a remedy/tool may be secondary.
Corpus context · 28 papers: median 6 main figures and 1 main tables; body span ≈ 10.19 pages; attached appendix span ≈ 14.87 pages (28 resolved boundaries). Separate source files: median 20 whole-file pages among 1 papers; these can repeat main text and are not appendix-only lengths. Extraction-assisted observations—not drafting targets.
Strong annotated analyses state an estimand or named question, progress through controlled alternatives, and use finding-led sections. They distinguish observation, fitted extrapolation and causal intervention, and preserve counterexamples and non-monotonic outcomes.
Editorial starting package: choose artifacts that test your actual claims. Quantities below are planning suggestions, not counts inferred from all category members or conference rules.
One concise view
Show what varies, what remains fixed, information access and the unit of analysis.
When: Critical when outcomes depend on conversation, intervention, population or resource definitions.
One clear entry point
Make the surprising relationship visible with axes, uncertainty and denominators.
When: Avoid showing only selected favorable examples.
One evidence destination per central explanatory claim
Eliminate competing explanations; separate correlation from a causal intervention.
When: Matched controls matter more than a fixed panel count.
A focused panel/table plus appendix details
Reveal scope across model families, budgets, tasks and non-wins.
When: Do not interpret a broad sweep as universal coverage.
Only when the analysis fits a model
Use distinct marks for measurements, fits and predictions and disclose sensitivity.
When: Extrapolated asymptotes are not achieved runs.
Complete prompts/configurations, sampling and exclusion rules, dated model versions, control variants, uncertainty procedure, all model/task results, failures and negative findings.
Use controls that distinguish conversational disclosure from rephrasing, then explain the percentile visualization before interpreting it.
Make each finding a question/control/result unit and keep mixture composition next to text and vision outcomes.
Differentiate measured models, fitted asymptotes and deployment compression; disclose sensitivity of nested fits.
Boundary: A new architecture or theorem is unnecessary when the contribution is reliable new empirical knowledge; novelty must be in the question, evidence or interpretation, not figure decoration.
See inline examples of these artifact styles · Compare every member in the measurement ledger
The primary reading route follows the annotated argument: Topological background before the experiment → Global and local analyses → Interpretation followed by controls. The opening distinguishes explaining geometry from detecting an attack; this is stronger than defining novelty solely through classifier accuracy.
The primary reading route follows the annotated argument: An interaction explains disappointing curricula → Moderate decay and checkpoint averaging → Illustrative theory and larger-scale appendix. The title poses a concrete mismatch, and the introduction turns earlier weak results into a testable interaction rather than dismissing curriculum learning.
Mixed contribution: The learning-rate/curriculum interaction is diagnosed before remedies are proposed. Classify by the explanatory experiment chain rather than only the final training recipe.
The primary reading route follows the annotated argument: Latent dynamics and theoretical remarks → Controlled models and foundation-model experiments → Limitations and appendix. Figure 1 makes the otherwise abstract vector field visible before equations; several random initializations communicate that one attractive image is not the sole example.
The primary reading route follows the annotated argument: Problem and controlled conversation simulator → Metrics, findings and causal hypotheses → Implications by audience and appendices. The paper names a recognizable failure mode, then operationalizes it with sharding instead of relying on anecdotes about chat quality.
The primary reading route follows the annotated argument: Intuitive compression viewpoint and formal logit matrix → Empirical low rank, transfer and generation → ISAN theory and future directions. The opening asks what a low-dimensional representation must preserve—future generation—and then contrasts multi-token structure with the familiar one-token softmax bottleneck.
Mixed contribution: The corpus observation about approximate low rank is distinct from exact rank guarantees for the simplified ISAN setting.
The primary reading route follows the annotated argument: Introduction / Problem formulation → Demystifying LLM visual priors: studies and findings → Scaling up and training a vision-aware LLM → Appendices A–M. The paper explicitly excludes a Bayesian interpretation of 'prior' and frames it as text-pretrained knowledge that helps later vision adaptation. Three contribution axes—structure, source and recipe—make a large empirical project navigable.
The primary reading route follows the annotated argument: Introduction / Problem Formulation and Preliminaries → Methodology → Experiments → Related Work / Conclusion / Appendices. The introduction explicitly chooses mechanistic understanding over production efficiency. It limits experiments to instruction-tuned autoregressive reasoning, explaining why complex search/backtracking models are not the initial target.
The primary reading route follows the annotated argument: Introduction / Data-constrained pre-training → Scaling parameters / ensembles / data → Distillation / Downstream tasks / Discussion → Appendices A–K. The title names an idealized resource regime, while the introduction contrasts fixed-data optimization with compute-optimal training. This makes additional training cost an intentional experimental tradeoff rather than an omitted efficiency metric.
The primary reading route follows the annotated argument: Empirical observations → Theoretical analysis → Limitations and potential questions. The opening asks when to communicate, contrasting temporal allocation with familiar graph design. Separate boxed question and phenomenon statements make the empirical discovery legible before formal machinery appears.
Mixed contribution: An observed communication-scheduling phenomenon precedes conditional theory; not a purely theoretical paper or only a systems speedup.
The primary reading route follows the annotated argument: Threat model and method → Evaluation → Conclusion and limitations. The threat model distinguishes malicious model weights from poisoned user data and input-triggered backdoors. Its central novelty is the timing of activation, which the related-work section contrasts explicitly with superficially similar attacks.
Mixed contribution: A new attack construction is retained under empirical auditing because the annotated narrative foregrounds the threat setting, stress tests and limits; it also changes training.
The primary reading route follows the annotated argument: A controlled-comparison question → Upstream, downstream and reproducibility detail. An explicit question about three resource constraints is more informative than an unqualified claim that sparse models are more efficient.
The primary reading route follows the annotated argument: Introduction and Background → MetamerGen and Behavioral Experiment → Results, Limitations, Discussion. The introduction makes an otherwise abstract internal-representation question experimentally tangible through visually different scenes receiving the same judgment.
Mixed contribution: A generative architecture is used as an instrument for a human-perception question, so the scientific empirical study is primary.
The primary reading route follows the annotated argument: Background and prevalence of divergence → Harmless versus pernicious divergence → Mitigation and limitations. Three introduction questions become successive sections: prevalence, consequences, and mitigation. This keeps a conceptual critique from becoming an undirected catalog of failures.
The primary reading route follows the annotated argument: Monitor Red Teaming definition → Numbered questions and findings. The paper explicitly distinguishes offline trajectory evaluation from online intervention before presenting reliability claims, preventing a detection benchmark from silently becoming a deployment-control claim.
The primary reading route follows the annotated argument: DNA analogy and formal definition → Practical extraction pipeline → Applications and stress tests. The biological analogy is immediately tied to two stated properties, then formalized. This gives a memorable organizing device, but the theoretical inheritance statement is conditional on functional closeness rather than proof of historical descent.
Mixed contribution: A functional representation is designed for model relationship analysis. The primary auditing classification does not turn conditional functional similarity into proven historical ancestry.
The primary reading route follows the annotated argument: Definitions and metrics before benchmark construction → Controlled task, model comparison and sensitivity checks. The question is compelling because ordinary benign requests, rather than explicit lying instructions, define the stress test. Synthetic contacts reduce dependence on memorized factual knowledge.
The primary reading route follows the annotated argument: Performance versus potential → Three consequences. The performance-versus-potential distinction gives the study a clear estimand: adaptation under a common recipe, not deployment-ready capability. The conclusion explicitly keeps direct evaluation as a complementary tool.
The primary reading route follows the annotated argument: Axes of variability → Experiments and dependencies → Additional experiments and templates. The central question concerns orchestration of frozen LLMs rather than a new graph encoder. The six variation axes help readers distinguish a systems comparison from model-training advances.
The primary reading route follows the annotated argument: Hidden shortcut problem and controlled environments → Effort proxy and detection tests → Discovery demonstration and limitations. The paper turns the mismatch between plausible explanations and shortcut answers into an operational question: how early can a truncated trace already earn proxy reward?
The primary reading route follows the annotated argument: Investigating reasoning-based IQA → Two designs from the interpretation. Two explicit questions—how reasoning relates to generalization and whether reasoning is needed at inference—organize the transition from analysis to a practical lightweight system.
Mixed contribution: The representation interpretation precedes two practical designs. The paper belongs to empirical analysis and learning, not exclusively either one.
The primary reading route follows the annotated argument: Binding problem → three-stage hypothesis → Representation → causal mediation → interventions. The red-square/blue-circle example defines binding concretely before connecting the question to VLM failures and earlier language-model binding IDs.
The primary reading route follows the annotated argument: Predictive framework and large-run demonstration → Forward ablations and assembled recipe → Scaling axes and robustness. The paper distinguishes a methodology for comparing scalability from a new optimizer. ScaleRL is explicitly an assembly of existing techniques; the novelty claim rests on systematic prediction and ablation.
Mixed contribution: The contribution is a scaling methodology and assembled recipe, not a newly invented optimizer; classify the systematic empirical comparison first.
The primary reading route follows the annotated argument: Behavioral stages to predictive RNN → Model prediction, biological comparison, alternative explanations. The introduction moves from known cell-development timelines to a specific missing mechanism. It explicitly positions the RNN as a functional approximation rather than a literal anatomical circuit.
The primary reading route follows the annotated argument: Loss is not downstream reasoning → Active compute and total TPP → Stress tests and limitations. The paper leads with a disagreement between improving pre-training loss and worsening mathematical accuracy. This concrete contradiction is stronger than a generic efficiency motivation and directly determines the experimental sweep.
The primary reading route follows the annotated argument: Measurable versus expressed preferences → Discover, describe and regress → Validation, conflicts and interventions. The central distinction between measurable and expressed preferences turns an ambiguous auditing goal into two separate questions. The probability factorization then links each question to a stage of dataset construction rather than relying on an interpretability slogan.
The primary reading route follows the annotated argument: A practical ranking question before a metric → Metric, controlled comparisons, practitioner decisions. The opening asks whether a tiny subset can distinguish two models, rather than merely reconstruct a leaderboard. This gives the proposed metric a concrete decision target.
Mixed contribution: A reliability metric supports a controlled micro-benchmarking study. This may also be browsed as measurement work; the primary empirical label follows the question-led findings.
The primary reading route follows the annotated argument: Define the observation gap → Evaluate detection and retained utility separately → Record limits and mitigation. The opening distinguishes overt safety failures from a mismatch between what a human interface displays and what is encoded in the underlying interaction. The threat model separates commercial adaptation access from full open-model control before presenting results. That explicit observation/access contract is the transferable writing lesson, not an assertion that every modern deployment has the reported vulnerability.
The primary reading route follows the annotated argument: Start with a reproducible failure → Diagnose from symptom to arithmetic → Intervene on the proposed mechanism. The introduction anchors the contribution in specific community-reported failures rather than a generic demand for faster transformers. It distinguishes an observed loss explosion from the proposed mechanism and the stabilizing intervention. The fixed recorded minibatch sequence is disclosed early, making the reproduction unit unusually concrete.
The main reusable deliverable is a corpus, evaluation suite, simulator, training environment, measurement instrument or data construction recipe.
Corpus context · 29 papers: median 5 main figures and 3 main tables; body span ≈ 10.26 pages; attached appendix span ≈ 21.9 pages (29 resolved boundaries). Separate source files: median Not resolved whole-file pages among 0 papers; these can repeat main text and are not appendix-only lengths. Extraction-assisted observations—not drafting targets.
The strongest resource narratives turn a missing evaluation/data dimension into a construction contract. They make taxonomy, provenance, filtering, labels, splits, baselines and the meaning of the score inspectable before the leaderboard, and validate the instrument separately from ranking models.
Editorial starting package: choose artifacts that test your actual claims. Quantities below are planning suggestions, not counts inferred from all category members or conference rules.
Start with one
Show source, filtering, annotation, splits, data access and evaluation roles.
When: Include attrition counts when filtering changes the claimed population.
One main summary plus detailed source tables
Make scale interpretable by categories, sources, task cells and comparison criteria.
When: More examples alone do not establish useful coverage.
A small, intentionally selected set
Show what annotators, users or agents actually see and how success is decided.
When: Label selection; examples do not estimate prevalence.
At least the tests needed for the stated validity claim
Test agreement, reliability, bias, sensitivity, leakage, or oracle correctness as relevant.
When: Human agreement is population/rubric-dependent, not universal truth.
One compact comparison plus targeted diagnostics
Expose unsupported capabilities and task-specific limitations alongside aggregate scores.
When: Metric direction, uncertainty, model versions and access assumptions must accompany the ranking.
Only if the resource includes a real formal/model contribution
Explain additional claims without obscuring provenance and measurement validity.
When: A dataset paper should not imitate a model-paper outline merely for appearance.
Datasheets/licenses and provenance, exact schema, collection/annotation prompts and rubrics, agreements and disagreements, filtering/exclusion lists, splits, evaluator code, model versions, full baseline settings and maintenance policy.
Each pipeline decision is experimentally justified; the flow view exposes attrition and domain mixture.
Organize data, tasks, metrics and baselines separately; document real acquisition rather than only a download link.
Define population, criteria and scenarios, then validate uncertainty, human agreement and population perturbations separately.
Boundary: A released benchmark is not a complete measure of intelligence, an empirical safety audit is not a safety guarantee, and an author screening process is not a legal guarantee.
See inline examples of these artifact styles · Compare every member in the measurement ledger
The primary reading route follows the annotated argument: OpenThoughts3 data pipeline → Scaling and conclusion → Detailed appendices. The contribution is a reproducible data recipe rather than a novel model architecture. The introduction names concrete unresolved choices and previews five findings that are then tested stage by stage.
Mixed contribution: A large empirical ablation study produces an open data recipe and release. Primary resource classification should not erase its empirical-analysis role.
The primary reading route follows the annotated argument: Threat and evaluation gap → Sandbox and benchmark construction → Decoupled evaluation, defenses, and end-to-end testing → Appendix artifacts. The paper defines the benign workflow, attacker control, and environmental gap before introducing the benchmark. The 9×24×4 construction makes the 864-example count interpretable rather than using scale alone as novelty.
The primary reading route follows the annotated argument: Data gap and related benchmarks → Tasks, datasets, metrics, baselines → Experimental questions → Acquisition and implementation appendix. The contribution list is divided into data, tasks, metrics, and baselines, and the rest of the paper follows that map. This suits a benchmark contribution better than imitating a new-model narrative.
The primary reading route follows the annotated argument: Classification needs both temporal patterns and cluster structure → Five-step synthetic generator → Q1–Q3 and extended evaluations. The gap is expressed as two data properties, not simply a claim that more synthetic data is useful; each motivates a distinct generator component.
Mixed contribution: Synthetic-data generation drives this contribution; the foundation model is the downstream test rather than sufficient reason to classify it as a new architecture.
The primary reading route follows the annotated argument: Informativeness rather than generic agreement → Objective, alternating refinement and exploration → Quality, validity, reliability and extensibility. A generic question and a contextualized variant make the motivation concrete before mathematical notation is introduced.
The primary reading route follows the annotated argument: Risk domains to controlled perturbations → Model suite, cross-domain findings and reusable benchmarks. The introduction identifies a methodological gap between small controlled studies and large observational studies, then explains why a released intervention suite closes it.
The primary reading route follows the annotated argument: Taxonomy creates the resource and evaluator → Generalization, independent benchmarks, utility. A concrete distinction between transfer to new tasks and transfer to new criteria makes 'generalization' testable rather than an umbrella claim.
The primary reading route follows the annotated argument: Real-world editing gap → Construction and evaluation → Appendices A–F. The contribution is defined by an interaction—existing code plus an instruction and IDE context—rather than simply another leaderboard.
The primary reading route follows the annotated argument: Two infrastructure gaps and benchmark construction → Benchmarking and agent training → Generalization, failures and documentation. The introduction distinguishes domain knowledge from executable biomedical coding, making the infrastructure contribution more concrete than a generic medical-agent claim.
The primary reading route follows the annotated argument: Benchmark construction and evaluation protocols → Interaction and simulator analyses. The opening concrete interaction motivates a change in the unit of evaluation—from isolated SQL to clarification, execution and state-dependent follow-up—rather than merely claiming another harder dataset.
The primary reading route follows the annotated argument: Introduction → Closed-loop interface → Evaluation / Discussion → Appendices A–G. The introduction asks whether beautiful predictions help an agent succeed, shifting the evaluation target from image quality to downstream utility. A scatterplot makes that mismatch concrete before the interface is introduced.
The primary reading route follows the annotated argument: Benchmark construction and evaluation → Video generation results and analysis → Appendices A–T. The opening taxonomy turns a vague physical-realism question into 10 categories, 50 subcategories and three prompt styles. The arithmetic behind 1,050 prompts makes coverage inspectable, although category breadth is not proof of physical completeness.
The primary reading route follows the annotated argument: Schema and conversion pipeline → Training and evaluation → Conversion and sampling appendices. The introduction reframes data scarcity as a format-interoperability bottleneck. Its contribution is a reusable representation plus converters and a corpus, not a novel agent reasoning algorithm.
The primary reading route follows the annotated argument: Define the object being measured → Validation before robustness. The opening makes subjectivity a design constraint and clearly defines the score as comparative consensus over three inputs, not an absolute attribute of a model.
The primary reading route follows the annotated argument: A missing axis of reliability → Controllable simulator to behavioral diagnostics. A concrete deployment mismatch motivates the benchmark: cloned apps hold design fixed while real interfaces vary. The contribution is a controllable simulator rather than simply a harder task collection.
The primary reading route follows the annotated argument: Framework, then interaction curriculum → Results, scaling analyses, and trajectory cases. The introduction distinguishes increasing internal reasoning from increasing environmental interaction; the method then targets the latter without conflating the two compute budgets.
Mixed contribution: Reusable agent-training infrastructure is treated as a research resource; the staged interaction curriculum and implementation are secondary types.
The primary reading route follows the annotated argument: Motivating counterexample and metric construction → Four research questions and matched appendix sections. A gravity example connects familiar physical variables to a causal graph and conditional-independence statements before contrasting conventional scores with structural violations.
The primary reading route follows the annotated argument: Gap and benchmark construction → Unified pipeline and process supervision → Research-question-led evaluation. The opening turns a vague reasoning goal into five annotated retrieval structures. Sub-question, modality, evidence and intermediate answer become explicit units of evaluation.
Mixed contribution: The multimodal search benchmark and process-supervision method are both substantive. Resource-first is an editorial primary classification.
The primary reading route follows the annotated argument: Clinician-grounded evaluation gap → Evaluation corpus → Adversarial extension. The paper clearly states a research-only boundary and explains why response quality, personalization and safety require distinct evaluation axes rather than a single accuracy score.
The primary reading route follows the annotated argument: Benchmark limitations → reproducible task and oracle → Capability measurements → practical impact → failure cases. Two explicit shortcomings—small evaluation sets and static outcomes—organize the introduction and map to the benchmark and discovery analyses.
The primary reading route follows the annotated argument: Benchmark setup and six research questions → Holistic auditing framework. A single practical question is decomposed into six numbered research questions. Repeated Motivation, Summary of Findings, and Detailed Results paragraphs provide a reusable pattern for a large experimental benchmark.
Mixed contribution: The benchmark and audit framework coexist with extensive empirical findings; resource-first classification is a navigation convention.
The primary reading route follows the annotated argument: Reusable platform, benchmark, verifier → Capability and resource-sensitive evaluation. The motivating gap is environmental change independent of agent action, not simply another harder task collection. Contributions distinguish the ARE platform, Gaia2 scenarios, and empirical study.
The primary reading route follows the annotated argument: Requirements to simulator and benchmark → Failure analysis and interventions. Everyday requests make operational dependencies and timing concrete before introducing the simulator. Three design requirements then organize the architecture, linking claimed gaps to implemented capabilities.
The primary reading route follows the annotated argument: Data infrastructure before agent design → Engineering evidence and replication details. The paper makes a defensible engineering contribution explicit: collecting consistent GUI trajectories across heterogeneous platforms is the bottleneck, rather than a claimed new model architecture.
The primary reading route follows the annotated argument: Interactive meta-evaluation gap → Rubric-grounded preference construction → Comparisons followed by error diagnosis. The benchmark's target is agreement with expert preferences, not an objective proof that a website works. The paper motivates dynamic interaction as the missing setting and defines an explicit query, two implementations and three-way preference tuple.
The primary reading route follows the annotated argument: What existing speech metrics measure → Factorized distribution distance → Human validation and recurring multilingual benchmark. The paper motivates cross-domain metric reliability through conflicting human-parity claims, then distinguishes matching-reference, nonmatching-reference and reference-free evaluation. This taxonomy makes the actual comparison conditions legible before new scores appear.
The primary reading route follows the annotated argument: Evaluation gaps become design requirements → Results with a layered audit trail. The introduction links each evaluation deficiency to a concrete contribution; Table 1 includes partial-coverage labels instead of treating all comparator suites as uniformly inadequate.
The primary reading route follows the annotated argument: From static tests to interactive evaluation → Arena, retrieval and failure analysis. The introduction identifies three concrete evaluation omissions—static challenges, isolated agents and missing quality gates—then ties contributions to an interactive protocol rather than claiming a new retrieval algorithm.
The primary reading route follows the annotated argument: Turn an infrastructure gap into an auditable artifact → Separate provenance from processing → Show composition and utility at different resolutions. The central argument is about data availability, provenance and reproducibility, not a new neural architecture. A four-criterion related-dataset table makes the intended contribution legible. However, ethical and legally compliant are broader claims than a dataset comparison can establish; writing should distinguish the authors’ screening process from a legal guarantee.
Start with one mechanism figure showing the baseline and changed path, one main comparison table with a clear data/compute contract, and one controlled ablation table or multi-panel figure that tests the central explanation. Add pseudocode when sampling, iterative updates or gradient stopping would otherwise be ambiguous. Add a quality–cost plot when efficiency is claimed, and a failure or generalization panel when the scope of deployment is important. These are a practical starting package, not six mandatory artifacts.
For cross-magnification distillation, explicitly show teacher and student input availability, physical field of view, magnification and any alignment assumptions. A low-resolution image is not automatically a lower-magnification acquisition. State what is removed at inference. Report teacher/student capacity and the training cost as well as the final inference benefit.
If the contribution includes a theorem, put its assumptions beside the guarantee and explain whether the implementation satisfies them. If the paper is purely empirical, a controlled mechanism diagnostic is more useful than adding an unrelated theorem. Keep configuration grids, complete per-task results, proofs and extra failures in a claim-indexed appendix; keep the defining method and validity conditions in the main paper.
