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Newest papers, page 65
Closed-Loop Dual-Reference Guidance for Accent Control in Cross-Lingual Voice Cloning
Zero-shot cross-lingual voice cloning often carries the reference speaker's accent into the target language.Don't Stop Long-Context Continual Pre-Training Too Early: An Empirical Scaling Law
Long-context continual pre-training (LCCP) is widely used to improve long-context capability, but how its benefits scale with the number of tokens used for LCCP remains unclear.Memory-Centric Continual Learning for AI Agents
Autonomous agents powered by foundation models can reason, plan, and use tools, but their fixed weights and context limits hinder sustained learning from experience over long-term deployment.Right On Q: Recovering Reasoning under Quantization via Self-Generated Trajectories
Quantization-aware training (QAT) aims to recover reasoning capabilities in low-bit large language models (LLMs).ECHO: Emergent Common-Mode Hidden Offsets Drive Vocabulary Collapse in Preference Optimization
Preference optimization can improve every ranking metric while building a hidden, shared representation shift that the relative loss barely sees, one that can eventually destroy generation.Beyond Small Talk: Modeling Disclosure Trajectories in Human-AI Conversation
Recently, we see a rise in conversational agents as a form of social and emotional support, where disclosure will unfold over time.CCG Diffusion: Combinatory Categorial Grammar-Driven Compositional and Syntactically Disambiguated Text-to-Image Generation
Text-to-image generation models do not explicitly receive the syntactic structure of the prompt, i.e., which word modifies which and where each phrase attaches.A KAN-based Non-linear Factorization for Embedding Layer Compression
Among existing embedding compression approaches, low-rank factorization provides a simple and mathematically principled solution by approximating the embedding matrix with a reduced-rank…BIRD-RL: Scaling Agentic Reinforcement Learning over Stateful Data-Centric Environments
While agentic reinforcement learning (RL) has emerged as a promising paradigm for training small language models (SLMs), existing frameworks are predominantly tool-centric and struggle with…DynaWeave: Learning to Adapt through Dynamic Scenario Synthesis
Large language model (LLM) agents increasingly use tools to complete tasks in stateful environments.Omnilingual-MsASR: Benchmarking Multimodal, Multilingual, Long-Form Multi-Speaker ASR of Omni Large Language Models
Omni-modal large language models (LLMs) have achieved remarkable progress in audio and video understanding.Harness Scaling for Generalizable Agentic Reinforcement Learning
Agent harnesses have become a central component of modern agentic systems, shaping how models use tools, manage context, and coordinate long-horizon interaction.OmniSimulator: Aligning Small Language Models for Authentic Heterogeneous Behavior Modeling
Large language models (LLMs) have shown great potential as general-purpose user simulators for interactive systems.TenetBench: A Clause-Anchored Russian Corpus for Evaluating Language Models against Professional Codes of Ethics
Codes of professional ethics guide and restrict specialists, who increasingly work with assistants built on large language models.A Strong MoE Is All You Need for Process Supervision
Process reward models (PRMs) provide fine-grained supervision for multi-step reasoning, yet obtaining reliable process labels remains a central bottleneck.Keeping Time vs. Understanding Time: Neither Alone Explains Temporal Reasoning in LLMs
Temporal reasoning is widely assumed to decompose into two modes: temporal arithmetic (date and time calculations), and temporal semantics (understanding temporal and causal relations).Mitigating Query Shortcuts in Chain-of-Thought with Context-Aware Decoding
While Chain-of-Thought (CoT) prompting improves the reasoning performance of large language models (LLMs), recent studies have shown that final answers do not always depend on the generated…Beyond the Vote: When Do LLMs Add Value to Retrieval-Based Tabular Classification?
Retrieval-based in-context learning (ICL) enables large language models (LLMs) to predict tabular labels from similar examples, but it remains unclear whether LLM inference improves on direct voting…pplx-embed-v2-late Technical Report
In this report, we introduce pplx-embed-v2-late, a pair of 0.6B and 9B multilingual and multimodal late-interaction models.RCSD: Reinforcement and Continual Learning for Domain-Specific Speculative Decoding
Speculative decoding accelerates large language model inference by employing a compact draft model to propose candidate tokens that are verified in parallel by a target model.