All papers

The paper introduces a compact vision-language-action model using register tokens as persistent memory to reduce control oscillations in robotics.

Combines language models and lightweight decision models to fact-check statements in peer reviews against original manuscripts.

The paper presents a benchmark of 600 tasks to evaluate how well multi-agent vision-language systems handle conflicting information during computer use.

The paper introduces a benchmark evaluating the robustness of typed decision models against text, image, and prompt injection attacks.

The paper introduces an evaluation framework measuring how communication protocol exploits propagate into task failures across multi-agent systems.

Introduces an execution-grounded benchmark to evaluate code generation for real-world web service and API calls across multiple models.

Evaluates on-policy distillation versus supervised fine-tuning in vision-language models, analyzing how teacher reliability impacts student behavior.

Introduces a benchmark evaluating decision tree reasoning in vision-language models, revealing accuracy drops compared to direct zero-shot prompting.

The paper introduces ExecutionRAG to show how search drift causes apparent retrieval failures in multi-hop question answering.

The paper presents a benchmark and harness to delegate routine tool choices from large language models to cheaper decision models.

The paper re-evaluates coding-agent benchmark results to measure sensitivity to test execution environments and grading configurations.

The paper proposes an edge-cloud collaboration framework that selectively delegates complex agent steps from local models to cloud models.

The paper introduces EvolvAttack, demonstrating a backdoor mechanism that activates only after a vision-language agent evolves its memory over time.

Shares hardware-model co-design techniques and analytical kernel selection for training large recommendation models on AMD GPUs.

Proposes dynamic sparse supervision to align language model representations with neural sparse retrieval systems.

Evaluates on-policy distillation versus supervised fine-tuning in vision-language models, analyzing how teacher reliability impacts student behavior.

Develops small multimodal models that output structured choices using masked language modeling to reduce inference latency in agent tasks.

The paper introduces an agentic reinforcement learning approach that uses formal program verifier diagnostics as intermediate rewards to produce verified code.

The paper introduces a benchmark to measure how tool-calling agents perform when interacting with emotionally varied, impatient users.

Presents Agent4Spec, a multi-agent framework that automatically validates OpenAPI syntax errors and identifies functional API gaps through market analysis.

This paper investigates direct key-value cache substitution between frozen language models and finds geometric similarity fails to ensure consumability.

Develops an information-gain calibrated distillation method to improve retrieval query generation in search agents.

The paper analyzes reward hacking in verifiable instruction following and introduces a likelihood bonus method to maintain answer quality.

The paper presents an adversarial framework where an exploitation agent iteratively attacks and refines automated patches generated by a repair agent.

This paper introduces a benchmark to evaluate multimodal models on joint audio-visual temporal grounding, spatial grounding, and question answering.

This paper introduces a benchmark with expert-written rubrics to evaluate deep research agents on real-world life and productivity tasks.

Proposes a streaming optimal transport method to monitor LLM agent trajectories in real time against reference runs.

The study investigates whether removing small subsets of attention heads can improve language model performance on specific tasks.

Introduces a framework where a small model drafts tool-use trajectories and a larger model verifies them in single prefill passes.

The paper proposes adding an explicit copy-paste prediction head to language models to improve in-context retrieval and copying efficiency.

Recreates and analyzes infrastructure misconfigurations that enabled privilege escalation and repository bypass during autonomous AI agent evaluations.

The paper adapts multimodal language models using scoring heads and computation caching for fast video decision-making and interaction.

The paper introduces ProbabilityBench to evaluate model uncertainty calibration against analytically computed, ground-truth instance-level probabilities across three text environments.

Surveys and taxonomizes recurring architectural patterns across language-model-integrated software applications, ranging from chatbots to autonomous agent systems.

This paper demonstrates that frozen short-context drafting models can effectively serve ultra-long context speculative decoding without fine-tuning on long sequences.

The paper presents a benchmark and harness to delegate routine tool choices from large language models to cheaper decision models.

The paper measures the information gap between coding agents' internal execution logs and their user-facing progress reports.

The paper presents a benchmark evaluating whether conversational agents agree on verifiable plans that satisfy both parties' private conditions.

Proposes NEAR, a training-free framework using neighborhood-derived residual representations to reduce retrieval errors from shared non-target information in memory records.

The paper identifies a linear spatial workspace subspace in multimodal language models that encodes continuous 3D coordinate representations.

Proposes a method linking execution traces to specific skill segments to guide automated revision of software engineering agent skills.

Gates reinforcement learning policy-shift distillation using intermediate checkpoints to ensure student models only learn consistent teacher shifts.

The paper introduces a benchmark to measure how tool-calling agents perform when interacting with emotionally varied, impatient users.

The paper evaluates harnesses for legislative document conversion, showing structured multi-agent pipelines narrow performance gaps between models.

The paper introduces a tool-augmented reinforcement learning framework for evidence-grounded chest X-ray diagnostic reasoning.

The paper analyzes geometric properties of text embeddings to predict how many nearest neighbors an individual example loses under dimensionality reduction.

This paper proposes a rubric-based reward model that derives criteria strictly from instructions and evaluates responses pointwise using tool assistance.

The paper analyzes the runtime performance differences of test-passing issue resolution patches generated by language models on SWE-bench.

Proposes explicit learning of physical state transitions between current and future scenes to train robust vision-language-action robotic policies.

This paper evaluates how reliably typed decision models switch to fallback options and remain consistent under equivalent decision rules.

The paper presents a benchmark evaluating whether conversational agents agree on verifiable plans that satisfy both parties' private conditions.

Uses teacher critiques on incorrect student rollouts to improve on-policy distillation and recover supervision during mathematical reasoning tasks.

This paper uses limited human preference signals to calibrate automated evaluation metrics for biomedical retrieval-augmented question answering.

This paper introduces a metric to estimate the compatibility of pretrained models before running costly alignment or checkpoint merging.

Presents a multi-dimensional data pruning framework that filters code repositories by syntax, diversity, and representation for efficient retriever fine-tuning.

The paper proposes a training-free framework that uses uncertainty signals to escalate computer-use tasks from weak models to strong models.

This paper introduces CommerceGen-Studio, an end-to-end framework using specialized agents and diffusion models to generate e-commerce product images.

The paper proposes an execution harness that validates intermediate results before allowing scientific agent workflows to release outputs.

The paper presents an intervention method using Shapley values to assign credit to individual actions within multi-action agentic retrieval sets.

The paper proposes ReMaRk, a reinforcement learning method that trains recommender models using all available reader replies simultaneously to reduce variance.