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Computation & Language / NLP
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Newest papers, page 198
Chinese-Jev: Bringing System One Model to Chinese-Language Tasks
System One models such as Jev offer an efficient alternative to generative language models for tasks that require decisions rather than open-ended responses.Repetition, Not Length: Isolating the Counting Failure in Neural Text-to-Speech
Text-to-speech models loop, truncate and lose count on text that repeats a phrase many times.AMU:Admission and Memory Update for Personalized Conversations---Structured Memory with SLM Guided Control
Large language models (LLMs) have become the foundation of personalized assistants, but maintaining persistent user memory across long-term interactions remains challenging.SRJudge: Empowering Large Language Models with Selective Reasoning for Fine-Grained Knowledge Concept Tagging
Knowledge concept tagging aims to assign specific concept or topic labels to educational content, which is essential for both educators and learners in traditional and online teaching practices.CypherTurn: A Multi-Turn Benchmark for Conversational Text-to-Cypher Evaluation and the Autonomy Divergence
Graph databases are increasingly queried through natural language, yet every existing benchmark evaluates isolated single-turn queries rather than the multi-turn sessions through which analysts…LatCom: Cross-Agent Latent Compression for Efficient Multi-Agent Collaboration
LLM-based multi-agent systems (MAS) increasingly use latent collaboration to avoid the information loss and repeated encoding-decoding overhead of natural-language communication.Selecting The Most Informative Tokens in Natural Language Autoencoders
Natural language autoencoders translate a language model's internal activations into readable explanations.Learning from Think-Mode Advantage via On-Policy Distillation
Explicit intermediate reasoning gives large language models (LLMs) a stronger problem-solving mode.Traverse: Learning When to Remember, Reset, and Redirect for Long-Horizon Web Search
Long-horizon information-seeking agents often accumulate noisy or misleading context, causing early mistakes to persist and making recovery increasingly difficult.What Does Post-Training Change in Multilingual Reasoning?
Open-source reasoning models provide unequal access to reasoning capability across languages.VACE: Validation-Gated Alternating Co-Evolution of Agent Models and Harnesses
Language model agents can be improved by updating their model weights or refining the harness that guides task execution.Unlocking the Critic: Reward-Free Policy Optimization for LLM Post-Training
Recent approaches to reinforcement learning (RL) post-training for large language models increasingly remove the critic to reduce training instability and memory overhead.Cross-Linguistic Effects in Bilingual Phoneme BabyLMs
Cross-linguistic effects are a central topic in bilingual first-language acquisition.LLM unbranding: Erasing Commercial Identity while Preserving Generic Utility
Establishing unbranding as a critical practice to prevent visual logos from acquiring negative connotations is standard in image generation.LoLBench: Evaluating Coding Agents with Long-Horizon Proposals on Large Software Systems
Modern coding agents can deliver increasingly large repository-level changes, and recent benchmarks reflect this by emphasizing long-horizon tasks with large reference implementations.Multimodal Detection of Higher-Order Behavioral Constructs: Self-Compassion in Structured Reflective Interaction
Many of the qualities that matter most in how people learn and grow, how someone regulates their emotions, reflects on a setback, or stays aware of others during a difficult conversation, are not…Trajectory Soup: Pushing the Compute-Scaling Frontier of LLM Mid-training via Diverse Trajectories
Mid-training equips pretrained large language models with specialized and reasoning capabilities, but the returns of this stage are bounded since additional serial compute yields little further…Bridging Semantic Gaps in RAG through Generated Context Knowledge Fusion
Retrieval-Augmented Generation has established itself as a fundamental framework in natural language processing, seamlessly integrating information retrieval with the generative capabilities of…VLM Fine-Tuning for End-to-End Combinatorial Optimization
Large language models (LLMs) have provided a unified interface for end-to-end combinatorial optimization (CO), but textual serialization alone may obscure spatial and relational structures that are…CredWise: A Controlled Agentic Decision-Intelligence Framework for Explainable and Auditable Credit-Risk Assessment
Credit-risk prediction is important in banking, but a prediction alone does not explain why an applicant is risky or how it should be combined with other evidence.