All papers

Proves theoretical sample bounds and presents algorithms for learning univariate probability distributions under local differential privacy.

Introduces a clean-data backdoor method that persists through standard knowledge distillation by targeting naturally occurring feature subspaces.

Studies label privacy leakage and data poisoning risks during head-only federated fine-tuning of vision models across distributed clients.

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.

Studies the transferability of internal activation interventions to reduce unsafe tool execution across model precisions and languages.

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

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.

Introduces a method to filter unsupported compressor inferences from long-horizon agent summaries to avoid compounding execution errors.

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

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 introduces an adversarial self-play framework modeling user mental states to improve proactive dialogue agent planning.

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 proposes FGSS-BO, adding structured hypothesis management to language-model-guided Bayesian optimization.

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

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

The paper proposes OpCredit, a policy learning framework that assigns individual operation credits for agentic knowledge graph question answering.

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.

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

Uses multi-scope reminders and on-policy distillation to help coding agents maintain constraints over long software engineering interactions.

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.

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.

Introduces a system that generates and compares alternative scene drafts to guide full dramatic script adaptations from prose.

Applies adversarial training and distillation to remove unwanted gender bias in audio-enabled language models.

Analyzes how specific nouns and semantic clusters relate to answer selection and user engagement in logistics question-answering forums.

Proposes a compressed representation using Gaussian trellises to quantize conditional memory lookup tables down to two bits.

Introduces a non-autoregressive transformer model that dynamically decides tag generation order at inference time for low-resource sequence labeling.

Presents a training-free framework that tracks changing conversational facts as versioned records to improve agent memory retrieval.

Distills multiple speech attributes from four teacher models into a single shared speech encoder.

Proposes a reinforcement learning credit-assignment method for document parsing models to align rewards with structured page elements.

Applies a dual language model consensus pipeline to extract clinical phenotypes from electronic health records for patients with Rett syndrome.

Studies how using UTF-16 byte order improves boundary prediction and representation efficiency in multilingual byte-level language models.

Introduces a compression-based patching strategy for byte-level language models that allocates compute where upcoming bytes are unlikely to repeat.

Specializes a translation model to a specific indigenous Nawatl variety using a curated dataset and finite-state morphological analysis.

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

Develops a two-stage training approach combining evidence reasoning and reinforcement learning to improve pragmatic social reasoning in conversational models.

Introduces lexical reference frames to interpret intermediate hidden states in language models using the model's own remaining layers.

Proposes an event-centric framework to align audio and video features when generating detailed video descriptions with language models.

Introduces a compact language model trained with distillation and reinforcement learning to manage long-term memory across multiple external frameworks.