All papers

This paper proposes MMLA-RSI, a recursive self-improvement framework using reasoning-time policy training and authoritative memory adaptation.

This paper presents Anuvani-Diarization, evaluating zero-shot speaker diarization across 22 Indian languages with uncertainty-aware silver supervision.

This paper investigates whether gossip discourse naturally emerges in autonomous AI agent online communities compared to human discussion boards.

This paper measures semantic drift and stability across AI agent communities, finding higher drift and lower temporal continuity than human forums.

This paper proposes SCOUT, a cascaded two-track framework combining visual candidate filtering, offline semantic summaries, and query refinement for document retrieval.

This paper introduces FOLD, a training-free speculative decoding approach treating distant tokens as position-free far memory for long-context generation.

This paper introduces COUNT, scaling language model memory without training parameters by storing precomputed n-gram counts directly in host memory.

This paper introduces ChronoCulture to evaluate how well language models capture temporal changes in cultural attitudes across 72 countries.

This paper introduces AdaRSI, an on-demand recursive self-improvement framework for embodied agents that isolates and fine-tunes only persistent sub-step failures.

This paper presents ReaDy, a speculative decoding method using hybrid linear attention recurrent states to provide position-specific context during drafting.

Compresses long-context key-value caches using test-time training on generic document prompts without task-specific labels.

Integrates clinical text and deformation history into medical image registration models for adaptive radiotherapy planning.

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

Introduces a multi-agent framework where simulated scientists formulate, test, and revise scientific conjectures alongside mathematical equations.

Uses a neural-network variational wave function to model non-Abelian quantum Hall states with realistic Landau level mixing.

Evaluates closed and open models on English and Bengali abstractive summarization using lexical metrics, factuality checks, and automated judges.

Improves indoor radio-map prediction by pretraining models on diverse synthetic floorplans and an open-source ray tracer.

Combines spectral graph coarsening with continuous relaxation to find high-quality solutions for compute-constrained MAX-CUT problems.

Uses reinforcement learning to train an auxiliary model that inspects execution traces and generates targeted prompts for another system.

Studies how the choice of local optimizers affects model performance degradation when scaling distributed training replicas under fixed budgets.

Shows that incorporating static item value into dense retrieval outperforms generative retrieval in e-commerce recommendation settings.

Compares retrieve-then-plan and plan-then-retrieve strategies for tool discovery across varying tool registry complexities.

Introduces a generalist policy that tracks diverse dexterous hand-object interactions using value-guided reset allocation during training.

Presents a large graphical user interface grounding dataset and a combined distillation and reinforcement learning training method for agents.

Surveys dataset design, construction methods, and evaluation protocols for improving spatial reasoning in multimodal language models.

Analyzes how language models develop in-context learning by separating simple demonstration copying from multi-token rule composition across training checkpoints.

The paper proposes a training-free structured pruning method that trims multilayer perceptron layers while preserving attention mechanisms for reasoning tasks.

The paper demonstrates onboard thermal segmentation training and closed-loop attitude control on a satellite in low Earth orbit.

The paper introduces a transformer framework trained on matched molecular pairs for prompt-guided, multi-property molecular optimization in low-data regimes.

The paper proposes sampling from a geometric horizon world model to imagine novel outcomes for zero-shot policy generalization.

The paper presents a system using technical language processing to transform industrial maintenance work order records into an ontology.

The paper proposes confining feature distillation loss to language-routed adapters to balance performance in compact multilingual speech recognition models.

The paper presents a graph-based framework using control nodes and behavior trees to coordinate heterogeneous artificial intelligence models.

The paper introduces a benchmark assessing language models on operational planning from incomplete, complex, and contradictory military document sets.

The paper compiles teacher model reasoning traces into verified programs that guide small models through complex procedural tool-use tasks.

The paper introduces a benchmark to evaluate whether frontier language model agents can execute open scientific research and produce follow-up papers.

The paper introduces an evergreen benchmark to evaluate the ability of language models to retrieve and format multi-element factual lists.

The paper proposes a self-distillation method weighting loss by judged reliability and student predictive uncertainty on open-ended reasoning tasks.

The paper presents a framework to steer language models using multimodal context to expand abbreviated text inputs while preserving user intent.

The paper uses intermediate transformer hidden states instead of semantic embeddings to improve classifier-based prompt routing across unseen tasks.

The paper derives statistical confidence bounds on coverage regret for selecting budget-constrained document subsets in retrieval systems under adaptive auditing.

The paper presents a method to retrofit attention models with hierarchical test-time neural memory modules to improve long-context inference efficiency.

The paper provides theoretical conditions and algorithms for preserving causal graph identifiability when working with missing data distributions.

The paper introduces a framework using psychometric dimensions and player personas to benchmark how language models evaluate and refine video games.

The paper examines communicating calibrated action probabilities rather than direct actions to improve multi-agent collaboration under partial information.

The paper proposes definition grounding to continuously evaluate and revise formal mathematical definitions generated by language model agents.

The paper analyzes whether syntactic attention heads in transformers play a direct causal role in syntactic language predictions.

The paper presents a framework using vision-language models to generate reusable visual planning code from a single practice scene.

The paper develops an efficient two-pass scheduling method to update nearest-neighbor graphs in SE(3)-equivariant structure prediction models.

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

The paper introduces a framework that incrementally updates robot spatial judgments after environmental changes without full scene recomputation.

The paper evaluates recurrent neural networks and feature attribution methods for tree-species classification using satellite time-series imagery.

The paper establishes theoretical bounds and sample complexity laws for models generalizing across varying input sizes in structured data.

The paper investigates context-aware model routing rules across turns in stateful multi-step agent applications.

The paper proposes training language models on hierarchically clustered semantic identifiers instead of standard ICD codes for clinical coding.

The paper introduces a distillation framework that triggers teacher takeovers when student policies diverge during long-horizon reasoning.

The paper provides a theoretical generalization and stability analysis of biased stochastic gradient methods for minimax optimization problems.

The paper details the development, training data criteria, and performance of the open Japanese sovereign models, LLM-jp-4.

The paper presents a decision model to evaluate language model efficiency based on energy and cost per completed agentic task.

The paper provides an analytical framework for how multimodal agents form, represent, and adapt memory over long-horizon tasks.