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Computation & Language / NLP
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The Self-Improvement Problem
With the rise of large language models, agentic systems are becoming increasingly autonomous, as they write code, call tools, revise prompts, and propose modifications to their own components.When Does "Post-Call" Begin? An Availability Contract for Earnings-Call NLP Benchmarks
Corporate financial disclosures often unfold over time, with announcements, prepared remarks, and discussion spanning multiple high-frequency return intervals.Enhancing Multimodal RLVR with Pairwise Token Contrast and Adaptive Trajectory Confidence
Reinforcement learning with verifiable rewards (RLVR) improves multimodal reasoning, but group-relative policy optimization broadcasts a single sequence-level advantage to every token of a response…Diffusion Language Models as Open Quantum Systems
Diffusion language models generate text by reversing a classical diffusion process over token sequences.No Single Tokenizer Feature Reliably Predicts Downstream Language Model Performance
Tokenization is a critical component of the language modeling pipeline, but it is poorly understood.Data Strategies for Large Language Models: A Unified Perspective on the Training Lifecycle
Data strategy plays a central role in training large language models (LLMs).Abstracts or full text? Evaluating input scope and computational efficiency in biomedical lay summarization
Biomedical research articles are mainly written for experts and often contain terms that are difficult for the general public to understand.Human Interventions as Labels: Localizing, Steering, and (Sometimes) Editing the Computations Behind LLM Agent Failures
When an LLM agent misbehaves, a human steps in, and that intervention is usually discarded or replayed as training data.FIRM: Fisher-Informed Retention Merging for Federated LLM Unlearning
Federated LLM unlearning addresses targeted knowledge removal when forget and retain data remain distributed across private clients and are inaccessible to the server.Retrieved but Not Read: Understanding Why Search-Augmented LLMs Fail to Use Available Evidence
Web search allows LLMs to access up-to-date information, but it remains unclear why they sometimes produce outdated answers even when relevant evidence is available.SceneWeave: Audience-Guided Scene Exploration for Dramatic Adaptation
Adapting prose into a script requires transforming narration and inner experience into scenes that convey relationships and psychological contradictions.Reliable Knowledge Acquisition for Continual Learning Agents
As agents powered by foundation models move into open-ended environments, continual learning from experience becomes essential.DeciCAD: Harness-Grounded Learning of Action Policies for CAD Agents
Large Language Model agents operate through closed-loop action policies whose decision space is defined by an agent harness.Bias Mitigation in Audio-Enabled LLMs through Adversarial Training and Distillation Methods
Audio-enabled large language models (AuLLMs) allow users to interact with them simply by talking to them, without the need for an intermediate transcription.In the Guise of Correction: How Framing and Cultural Adaptation Erode LLM Safety on Real-World Criminal Cases
Existing safety benchmarks rely largely on synthetic prompts with loosely grounded taxonomies and offer little focused analysis of severe crime categories.Behavioral Forgetting Is Not Privacy in Machine Unlearning
Machine unlearning aims to remove targeted information from large language models, but existing evaluations often measure forgetting only on a designated benchmark distribution.Content associations with answer selection and engagement in supply chain community question answering
Community question-answering platforms are important ecosystems for knowledge sharing, yet research on what makes an answer best or a question engaging has often focused on either individual lexical…RoleQ: Role-Aligned Scales for Converting Language Models to Ternary
Weight quantization below four bits shrinks memory and bandwidth at a perplexity that looks acceptable, but often at the cost of factual knowledge and task accuracy.N-Gram Memories at Two Bits: Quantizing Lookup Tables in Large Language Models
Conditional memory expands language-model capacity through selective lookup, but the full embedding table must remain available for every possible input.Dynamic ordering in sequence generation in Transformers: experiments on low-resource Tagging scenarios
This paper introduces TagInsert, a Transformer-based model that challenges the common left-to-right (L2R) generation paradigm in Transformers, for sequence labelling tasks where dependencies may not…