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Computation & Language / NLP
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Newest papers, page 181
Distilling Directional Verification
Knowledge distillation aims to transfer the factual knowledge of large language models to smaller models for efficient deployment.Scaling and Distilling Text Embeddings for Better Diffusibility
Diffusion language models (DLMs) offer a promising alternative to autoregressive (AR) language generation.Pay for the Fault, Not the Flow: Label-Free In-Flow Multi-Agent Workflow Optimization
Large language models (LLMs) increasingly construct multi-agent workflows that decompose a complex task and assign specialist agents from a pool.Groundability, Not Scale Alone: When Weak Reviewers Can Audit Strong Coding Agents
Coding agents can return plausible patches that omit required behavior.It Takes Workflows to Evolve Better Workflows
Tackling complex real-world tasks can exceed the capabilities of a single large language model (LLM), motivating the use of multi-agent workflows that coordinate specialized agents to work together…LawCompass: Navigating from Legal QA to Multi-Agent Deep Research with Grounded Evidence
Recent advances in Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) have significantly democratized access to legal information.Beyond Final Accuracy: Auditing Communication in LLM Multi-Agent Systems
Multi-agent communication aims to help agents benefit from one another's information.Empty Commitments: When Agents Promise What Their Runtime Cannot Deliver
A chatbot that says "I will remind you tomorrow" will not run again until the user writes.Sentence Specificity Scores for Collaborative Technical Documentation: A Domain-Transfer Study
Collaboration depends on shared context, and technical documentation is one way that context persists across people and AI teammates.Capturing In-Context Learning Dynamics with Task Operators
In-context learning (ICL) enables language models to perform new tasks from demonstrations without weight updates.JoinGR: Learning to Traverse Join Graphs for Table Retrieval
Retrieving the right tables is a prerequisite for Text-to-SQL over realistic databases.Probe with Participation Trophies: Random-Reward RL as a Probe of LLM Capability
We connect the spurious-reward paradox to a model's reachability and propose random-reward reinforcement learning (RL) as a useful tool for the probing enterprise, addressing a decade-long debate…Precision over Scale: A Polish-Silesian Benchmark and a Translation System Outperforming Open-Source and Commercial Models
Dialectal machine translation remains challenging due to limited data and strong linguistic variation not captured by standard benchmarks, which often assume standardized and well-edited text.AgSpec: Pushing the Limits of Retrieval-Based Speculative Decoding in Coding Agent Pipelines
Retrieval-based speculative decoding (SD) drafts tokens by copying continuations from existing text, which suits coding agents that repeatedly reproduce code, logs, and earlier attempts.Madeleine: Learning Involuntary Recall for Conversational Memory from Simulated Lives
A long-term conversational assistant must recall the right memory at the right moment, yet the memory that matters most is often not similar to what the user says now.Counting and Min-Cost Encoding for Tokenization in Large Language Models
Mainstream large language models rely on a tokenizer to encode text into a token sequence.Do Multilingual Encoders Produce Language-Consistent Semantic IDs?
Semantic IDs (SIDs) compress item embeddings into discrete code sequences used in generative retrieval.BanglaDial-Abuse: A Corpus-Grounded Dataset for Regional Dialect Identification in Abusive Bangla Text
Regional linguistic variation remains an important challenge for Bangla natural language processing, particularly in informal and non-standard text.My FAULT: Self-Diagnosis as Credit Assignment in Self-Evolving Agentic Reinforcement Learning
Agentic reinforcement learning (RL) has emerged as a powerful approach for training large language model agents on multi-step tasks, yet reliance on terminal outcome rewards creates two…Persistent Depth Ordering amid Shifting Block-Bypass Responses in Language Model Pretraining
Layer interventions are widely used to probe the internal organization of language models, yet most analyses examine a single training checkpoint even though model representations and computations…