All papers

The paper presents open-weight language models adapted for the Wolof language using instruction tuning, preference alignment, and model merging.

The paper proposes a method to translate reverse reasoning paths into verified forward chains of thought for distillation.

The paper introduces a confidence-calibrated controller to optimize inference compute and test-time expansion decisions in reasoning models.

The paper investigates performance vulnerabilities in self-improving agents when their co-evolved skill banks undergo post-deployment drift.

The paper analyzes why reinforcement learning with majority-vote rewards provides effective training gradients despite noisy individual labels.

The paper proposes a non-elitist evolutionary optimization framework that retains and iteratively refines low-reward agent skills using execution feedback.

The paper presents a decision model that performs deeper reasoning by looping its layers instead of generating output tokens.

The paper studies how semantic layers, orchestration, and compaction impact the performance and behavior of data agents across benchmarks.

The paper uses latent eigenvectors from CLIP to select smaller, representative training datasets for diffusion models.

The paper investigates when language models learn capabilities by tracking unembedding matrix dynamics during pretraining using parameter-trajectory crosscoding.

Presents a multi-agent system for paper screening and data extraction in systematic literature reviews following standard guidelines.

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

Develops an open agent environment for evaluating how autonomously language agents can conduct post-training machine learning experiments.

Estimates language model ensemble member reliability from unlabeled agreement patterns using an expectation-maximization jury formulation.

Introduces an equivariant self-supervised learning framework that avoids topological collapse in 3D point cloud masked autoencoding.

Uses reinforcement learning to train an agent module that selects, sizes, and orders retrieved memories for downstream tasks.

Measures tool-agent performance failures under controlled harness limits such as completion caps and tool menu visibility.

Analyzes trade-offs between aggressive and conservative planning horizons in language models operating under partial observability.

Introduces a dataset benchmarking human-agent interaction and collaboration across long-horizon professional workflows.

Examines failure modes in label-free evaluation where models reach high consistency across perturbations while generating identical incorrect answers.

Develops a structured explainable reasoning architecture to extract sensitive self-disclosures from cybersecurity interviews using local small language models.

Analyzes working memory error patterns in language models to show that failures stem from readout interference rather than target retrieval.

Proposes an ensemble framework that weights judge confidence and masks disagreements when training models with rubrics as rewards.

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.

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 presents an agent system that generates, edits, and renders interactive virtual worlds from text using executable code rules.

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

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

Creates a structured schema that converts research papers into connected scientific entities to improve retrieval-augmented generation for autonomous agents.

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.

Surveys the application of artificial intelligence methods to humanitarian disaster response and displacement monitoring.

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.

The paper analyzes why search-augmented language models produce outdated answers even when relevant evidence is retrieved.

Proposes a structured knowledge admission and verification system to prevent continual learning agents from storing harmful past experience entries.

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

Combines multi-layer vision transformer representations with a fusion decoder to localize pixel-level generative image inpainting manipulations.

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 trains feedforward 3D reconstruction models using privileged motion and gravity cues to improve dynamic scene understanding.

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