All papers

The paper evaluates how language models correct user misconceptions across five languages, showing indirect pushback often fails to fix factual errors.

The authors propose decoupled positional encodings enabling full-duplex speech models to run internal thinking streams at hardware-adaptive speeds.

Studies reflective prompt optimization in research agents, demonstrating that gains on held-out tasks can stem from prompt-level memorization.

Proposes a training method to adaptively skip turn-by-turn agent reasoning when previous thoughts remain sufficient for future actions.

The paper proposes a code summarization method that encodes abstract syntax tree paths using self-supervised learning to reduce training overhead.

Evaluates AI text detectors under attacks applied to both human and machine text, revealing high false-positive rates on edited human writing.

Presents a randomized experiment framework to test and curate self-written agent playbooks, preventing task failure caused by unverified lessons.

The paper introduces ViMoD, maintaining a compact visual context while dynamically routing back to fine-grained tokens during multi-step reasoning.

Introduces a post-training framework that trains text-to-SQL agents directly inside interactive database execution environments.

The paper proposes compression-layer modifications to reduce accuracy loss when using dynamic LZW vocabularies during language model inference.

The authors combine zero-shot test suite generation with agentic refinement to improve test coverage and mutation scores.

Shows linear prompt-injection monitors overfit to conversation features and reduces their false alarms by adding matched harmless negative examples.

The paper presents a simulated benchmark across eleven unconstrained environments to evaluate vision-language models on documenting real-world human physical work.

This paper introduces Choice2Seq, a benchmark measuring how often language models retain correct multiple-choice answers when generating them step by step.

Evaluates whether language models can act as world models to predict empirical experimental outcomes across AI research workflows.

Presents a symmetry-aware value cache merging method to compress key-value memory during long-context decoding without architectural changes.

This case study evaluates a non-programmer using an AI development tool to build a database application in a non-English language.

The paper repairs multimodal chain-of-thought final answers using original image features without altering the generated reasoning trace.

Demonstrates an attack method that alters embedded multi-bit text watermarks without knowing the secret generation key.

The paper examines interpretive lock-in where planning agents generate code passing self-generated tests while missing the actual specification requirements.

Introduces an instruction-following benchmark and training recipe to improve goal adherence in full-duplex conversational models.

Designs an inference serving system optimized for dynamic, token-level routing across multiple language models.

The paper evaluates pre-trained code models under a standardized experimental setup to compare the impacts of their pre-training tasks fairly.

This paper introduces a system to ensure tool-using language model agents comply with natural language company procedural manuals.

The paper presents Diffuplex, a full-duplex spoken language model that separates speech timing from content generation using discrete diffusion.

The paper evaluates long-term memory gating models to test whether they distinguish established factual updates from mere suggestions.

Presents a rolling masked diffusion method that accelerates full-duplex spoken dialog models by forecasting multiple future interaction frames.

Proposes a co-evolution framework that coordinates agent harness prompt modifications with model weight updates using self-generated experience.

The paper examines how prompting conditions and automated agent reviews affect code complexity placement across two software architectures.

The paper presents a theoretical framework using nesting properties to calculate distance bounds and compare language model truncation sampling algorithms efficiently.

This paper analyzes label collapse failure modes across diffusion and autoregressive language models during in-context learning classification.

This paper develops an uncertainty estimation framework for omnimodal models using external embeddings to score reasoning grounding and coherence.

The paper introduces an executable contract and monitor to verify that delegated authority revocations actually take effect across distributed agent gateways.

The paper describes training a competitive decoder-only search reranker from scratch using modest computational resources and common datasets.

This paper uses activation steering and probing to identify distinct internal representations for rule-based versus consequence-based moral reasoning in language models.

This paper introduces an environment benchmark and exploration framework to help graphical user interface agents learn unfamiliar interaction dynamics.

Introduces a medical benchmark of real de-identified patient edge cases pairing imaging with clinical vignettes to test multimodal language models.

This systematic review analyzes security vulnerabilities, benchmark shortcomings, and developer oversight challenges in code generated by language models.

The paper evaluates long-term memory gating models to test whether they distinguish established factual updates from mere suggestions.

The paper demonstrates that entropy-based byte patching misses predictable calculation points and proposes a boundary-dependence signal to fix it.

Investigates attributing unsafe code changes to specific agents or versions in self-evolving multi-agent coding systems.

The paper presents OpenMobile-2, combining open mobile environments, trajectory datasets, and hybrid GUI-tool actions for mobile agents.

This paper presents a bilevel distillation method that weights teacher supervision tokens based on their predicted impact on student reward.

This paper proposes TAPS, combining standard accuracy training with post-hoc confidence selection to give controllable abstention rates without specialized tuning.

This paper evaluates pretraining on chemical language models across 64 molecular datasets, finding that gains saturate below gradient-boosted tree baselines.

This paper trains language models using reinforcement learning to construct executable financial trading formulas grounded in research literature.

This paper presents a fine-tuning method to update language models for code comprehension as codebases change over time.

Introduces a risk analysis framework and test environments for enterprise-level multi-agent workflow interactions.

This paper introduces a benchmark evaluating multi-agent organizational structures across diverse domains and collaborative interaction patterns.

The paper proposes a structured pruning method to compress vision foundation models into compact task-specific models under parameter budgets.

This paper introduces RelLedger, an auditing framework to trace how relational histories drive reporting choices and actions in multi-agent systems.

Introduces a model-agnostic, concept-level attribution method for black-box vision models by masking concepts located with vision-language models.

Presents an end-to-end distributed system orchestrating coding agents to execute computational research experiments alongside human scientists.

The paper introduces a structured experience graph memory that organizes and maintains agent workflows across hierarchical abstraction levels.

This paper frames language model evaluation as a forecasting problem, using time-series techniques on historical data to predict post-deployment performance.

The authors present an autotuning framework for WebGPU kernels to improve cross-platform inference speeds for language models on edge devices.

MedDEx combines language models for patient data extraction with deterministic rule engines to execute clinical treatment guidelines accurately and efficiently.

Investigates multimodal LLM failures on squint-readable low-frequency text and provides a diagnostic benchmark alongside training interventions.

Evaluates whether dependency graphs improve automated mapping between source code files and system architectures.

The paper evaluates delegating code-repair decisions to low-cost probabilistic judges to reduce token costs without losing accuracy.