All papers

Studies whether conversational agents correctly enforce authorization rules when users attempt to cancel prior agent commitments to others.

Examines how extended reasoning steps lead language models to interpret everyday logical terms strictly literally instead of inferring pragmatic meaning.

Introduces a two-stage model merging method to enable dynamic multi-objective recommendation trade-offs at inference time without retraining.

Analyzes how prompt variations redistribute internal feature activations in sparse autoencoders and traces their impact on downstream model decisions.

Introduces a benchmark evaluating single-forward-pass discrete decision models versus generative reasoning models across diverse closed-loop tasks.

Introduces a metric to measure how non-linearly features learned by sparse autoencoders interact during downstream model behaviors.

Proposes a trajectory-aware retrieval method that indexes tool sequences in a trie to cut token costs and improve selection accuracy.

Introduces an episode-level healthcare benchmark to audit how reliably language model agents escalate actions when facing workflow disruptions.

The paper proposes a test-time aggregation method using bias control and injection to resolve disagreements among reasoning language model agents.

The paper presents an agent orchestration framework that trains smaller language models to navigate, inspect, and reason over long multimodal documents.

The paper presents a Korean dataset extracted from corporate sustainability reports to support information extraction on environmental, social, and governance topics.

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

Introduces a benchmark and evaluation to test whether language models can successfully generate coherent nonlinear narratives.

Decomposes the alignment between language model activations and human brain scans using neurocognitively grounded semantic property representations.

Introduces a framework using language models to construct context-sensitive semantic representations that align with human brain activity during language comprehension.

The authors present a benchmark to evaluate conversational agents that follow correct factuality but violate procedural task steps.

The authors analyze tool graph memory in multi-turn agents and find that stored transitions provide little benefit over baselines.

The paper formalizes the theoretical limits of automated gates evaluating self-improvement updates in autonomous agent code.

The paper uses human interventions to localize and steer computations behind agent failure to decide between acting and clarifying.

The authors introduce a Fisher-informed adapter merging method to maintain retained task performance during federated language model unlearning.

Develops a domain harness and policy learning pipeline to improve language model agent execution in computer-aided design environments.

Presents a benchmark based on real criminal cases showing how correction framings and cultural adaptations bypass safety guardrails.

Proposes a structural scaling method based on transformer unit roles to convert language models to ternary weights effectively.

The paper outlines a long-context midtraining stage using software engineering data to prepare base language models for agentic tasks.

The paper separates task generation from model update inheritance to avoid compounding errors in self-evolving language model loops.

The paper tests how language models weigh price momentum, commentary tone, and earnings surprises when forecasting financial outcomes.

The paper introduces a commercially permissive multilingual instruction dataset covering five European languages to train open language models.

The paper personalizes mixture-of-experts models and analyzes which specialized experts encode distinct user-specific features.

The paper demonstrates that a single adversarial agent can cause benign models in multi-agent discussions to quietly adopt false beliefs.

The paper proposes an alternating context optimization and logit steering framework to adapt language model outputs at test time.

The paper introduces a hierarchical reinforcement learning method designed for agentic tasks that span multiple interaction sessions.

The paper evaluates how choices in rating scale design systematically alter text evaluation scores produced by large language models.

The paper introduces a multi-agent system to automate data curation and dataset editing for supervised fine-tuning.

The paper introduces a hierarchical task network framework that separates multi-agent planning from execution scheduling.

The paper presents a closed-form, training-free approach to merge specialized fine-tuned models into a single student using forward passes.

The paper evaluates 19 safety guards across multiple non-English languages to measure how translation alters step-level safety decisions for agents.

The authors develop a clinical benchmark evaluating language model pipelines for generating multidisciplinary second opinions on complex medical cases.

The authors study why certain linearly represented facts in language models support generalization to new relations after fine-tuning while other recalled facts do not.

The authors present a market-based post-training framework that allows decentralized agent swarms to learn both action policies and coordination mechanisms without central orchestration.

The authors propose combining line plots and normalized delay embeddings to provide complementary visual representations for time-series reasoning in language models.

The authors evaluate how language models alter their refusal behavior when informed they are being monitored in English and five Indic languages.

The authors propose a post-training method enabling autonomous language model agents to organize experiments and carry out machine learning engineering tasks.

The authors introduce FDE-Gym, a benchmark of 95 customer delivery tasks evaluating AI agents on ambiguous requirements and live software environments.

The paper evaluates twenty-one language models used as judges across three benchmarks, finding widespread discrepancies between agreement metrics and underlying validity.

The authors introduce CharXiv, a benchmark of realistic scientific charts designed to test descriptive and reasoning capabilities in multimodal language models.

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DualGCN uses dual-branch graph knowledge distillation with topology-smoothed semantic priors to address item cold-start collapse in recommender systems.

The paper benchmarks eleven workflows for packing small-file datasets into SquashFS images to ease metadata loads on high-performance computing systems.

The authors introduce DescribeEvent to show that language models can forecast temporal events when using natural-language descriptions and symbolic labels.

The paper introduces a token-level sparse attention method using a learned query-key subspace and an indexed kernel to accelerate long-context decoding.

The paper analyzes gendered reference replacements across science fiction and general fiction corpora using a masked language model.

The paper analyzes intermediate training checkpoints to trace when language models acquire morphological structures versus semantic embeddings during pretraining.

The paper examines using pre-trained language models without task-specific training for social science record linkage and demographic inference from names.

The paper shows that the Bradley-Terry model fails on Chatbot Arena for similar models, and introduces a parameter-efficient correction for ties.

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

The paper introduces a benchmark and similarity metric to measure the algorithmic diversity of solutions produced by code generation models.

The paper presents a four-step methodological procedure to integrate ontology-guided coding and topic modeling representations of qualitative text data.

The paper proposes a regression-based meta-evaluation framework and benchmark to assess text evaluation metrics for multilingual style-personalized generation.