All papers

The authors introduce a training loop where multimodal models use text and visual abilities alongside program execution to generate their own training data.

The paper introduces a method to reduce redundant external knowledge retrieval by estimating what a language model already knows.

This survey paper reviews attacks, defenses, and evaluation methods across 135 studies on secure retrieval-augmented generation systems.

This paper introduces an adaptive framework to detect radio jamming attacks within machine learning integrated 5G Open RAN architectures.

This position paper proposes multi-agent reinforcement learning and mechanism design to reform academic peer review processes.

This paper surveys foundation models, agent systems, datasets, and computational tools applied to materials science discovery.

This paper presents a control and communication framework using reachable sets and barrier functions to guarantee multi-agent safety during intermittent communication.

This paper introduces a posetal game framework for auditable multi-agent motion planning under partially ordered legal constraints.

This paper establishes analytical geometric bounds on coverage probability and timing uncertainty for multi-agent drone meshes intercepting approaching objects.

Analyzes rational strategy synthesis and equilibrium guarantees for stochastic multi-agent systems with probabilistic temporal logic objectives.

Introduces an auditing framework that checks counterfactual execution traces to ensure language model routing uses only legitimate information.

Proposes a hybrid architecture combining global state-space modeling with local transformer attention and lexical memory for efficient inference.

Presents a benchmark with open-ended tasks and rubrics to assess social awareness and response generation in Chinese.

Introduces an off-policy reinforcement learning algorithm for language models using verifiable rewards without importance sampling weights.

Introduces a benchmark combining natural, scripted, and synthetic audio to evaluate phonetic error detection in speech models.

Tests whether linguistically accurate morpheme boundaries in tokenizers improve translation performance compared to scrambled control segmentations.

Proposes a hierarchical multimodal transformer to model long-term behavioral changes across multi-session interactions for cognitive impairment detection.

Evaluates a multimodal screening method using voice, video, and text from voice-assistant interactions to detect cognitive impairment.

Presents a benchmark with human-annotated visual evidence to evaluate retrieval and question answering across long documents.

Deconstructs multiple-choice option bias in language models into two factors tracking answer flips and positional skew.

Explores methods to extract the underlying decisive sub-questions that drive language model decisions across diverse generated outputs.

Proposes an adaptive reasoning framework that supervises intermediate retrieval states and stops reasoning steps dynamically to cut latency.

Introduces a benchmark to test model generalization under systematic convenience sampling biases in tabular data.

The paper proposes SHIP, an architecture for extracting structured differentially private representations from text for analytical queries.

The paper examines AI-generated text detection in scientific papers by combining visual formatting, PDF text, and LaTeX representations.

The paper proposes RoPERelay to reorganize positional geometry and rotary embeddings after sparse KV cache token eviction.

The paper introduces a causal intervention framework to distill transferable self-generated in-context learning demonstrations.

The paper presents an automated speech recognition and sentence completion system to assist dementia patients speaking Bengali.

The paper surveys memory-centric compression techniques for managing inference resources and context windows in large language models.

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

The paper introduces the Korean Benchmark Suite to evaluate foundation models across six natural language capabilities.

The paper audits data decontamination methods on math corpora to quantify false removals and benchmark leakage risks.

The paper presents a teacher-student framework using language models for weak annotation to classify cybersecurity tweets.

The paper proposes isolating internal model judgments from instruction formatting variations using a label-free hidden state subspace.

The paper introduces TimeCap, an open deterministic pipeline for generating signal-grounded captions from wearable health time-series data.

The paper evaluates output non-determinism in commercial language model deployments during medical device regulatory compliance assessments.

The paper introduces VALCON to evaluate and improve value consistency when language models alternate between generator and evaluator roles.

The paper presents PocketTSLM, deploying an on-device soft-prompt time-series language model on smartphones for personal health data.

The paper introduces ZoomCap, an adaptive visual zooming and evidence-routing framework to improve fine-grained image captioning supervision.

The paper introduces a hybrid generative-tagging architecture to improve efficiency and coverage in product attribute and value extraction.

The paper presents VoteGEO-Bench to measure how rewriting web content influences information visibility in generative search engine election queries.

The paper introduces QuasiLeak-Bench to evaluate whether privacy detectors can identify distributed quasi-identifiers governed by disclosure policies.

The paper studies ranking and partitioning attention heads by gradient norm for sparse and continual reinforcement learning fine-tuning.

The paper presents an internal attention boosting method to improve how language models infer implicit conversational affective states.

The paper introduces RenoLayout to generate residential renovation floorplans and furniture layouts under structural and physical constraints.

Introduces a dynamic precision control framework that allocates weight chunks to mixture-of-experts models during inference.

Analyzes how calibration language choices affect block-influence depth pruning rankings in multilingual language models.

Proposes a factor-wise representation framework to selectively treat different distribution shifts in multimodal machine learning.

Evaluates prompt optimization methods and identifies effective prompting strategies for social media stress and emotion classification.

Introduces a hybrid speculative decoding framework splitting draft tasks between mobile neural processing units and cloud servers.

Develops a lightweight situational distortion metric to evaluate speech recognition errors based on practical conversational severity.

Identifies translation circuits in multilingual models and optimizes fewer than five percent of parameters for low-resource translation.

Conducts a controlled evaluation of direct knowledge injection into addressed conditional memory modules across small and production models.

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

Introduces a data augmentation method that breaks spurious contextual correlations causing emergent misalignment during domain finetuning.

Identifies a specific vision transformer attention head that spuriously attends to medical image corner text instead of lung pathology.

Proposes an availability framework to identify and resolve within-event timing mismatches in earnings-call financial NLP benchmarks.

Improves reinforcement learning credit assignment in multimodal reasoning using pairwise token contrast and adaptive trajectory confidence.

Frames diffusion language models as open quantum systems to enable text generation conditioned on superpositions of prompts and thoughts.

Shows across 200 models that proposed intrinsic tokenizer metrics do not reliably predict downstream language model performance.