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TEM: Temporal State Tracking Memory for Large Language Model Agents
An agent's memory must remain useful as information changes across conversations.Learning Speech Attribute Representations via Multi-Teacher Distillation
Recent post-training approaches have shown that self-supervised speech encoders can be adapted to learn a single utterance-level speech attribute through knowledge distillation.P2TPO: Structure-Aligned Page-to-Token Credit Assignment for Full-Page Document Parsing
End-to-end full-page document parsing must preserve the content and structure of heterogeneous elements, yet otherwise accurate outputs can contain local errors and omissions.Priming Base Models for Agentic Post-Training: An Open Midtraining Recipe
Frontier models, general-purpose and domain-specific alike, now include a midtraining stage between pretraining and post-training, but little has been published about how this stage is built.Selection Is Not Learning: Choosing Model Futures
Self-evolving language models train on tasks they generate, so every round must decide which updated model the next round inherits.WEAVE: Weaving Real-World Code Evolution into Dependency-Structured Tasks for Benchmarking and Training Code Agents
Real-world software engineering rarely consists of isolated changes.Momentum, Commentary Tone, and Earnings Surprise in LLM Financial Forecasts
Which inputs drive a language model's financial forecast?Effectiveness of Hidden-State Probes for Localizing Reasoning Failures
Verifying the final answer of a long reasoning trace may show that the trace is wrong but it does not distinguish three cases.Name-Bias in Large Language Models
Names carry demographic indicators such as age, gender, and race, which makes them a subtle but strong target of bias in large language models (LLMs).Documenting Trofinetide Treatment Phenotypes in Rett Syndrome with a Dual-LLM Consensus Pipeline
Trofinetide, approved in 2023 as the first Rett syndrome treatment, lacks scalable real-world monitoring because chart review is labor-intensive.Beyond Context Length: Characterising the Difficulty of Long-Context Reasoning
Long-context language models support ever longer context windows, and evaluations typically treat context length as the main indicator of difficulty.The Right Byte First: How UTF-16 Helps Multilingual Boundary Prediction
Many hierarchical byte-level language models group bytes into patches using a learned boundary predictor whose view of the next codepoint depends on encoding and lookahead.Multi-SFT: Permissively-Licensed Multilingual Instruction-Following
Supervised fine-tuning datasets remain overwhelmingly English-centric and commercially restricted, as their content is frequently generated by proprietary models whose terms of service forbid…Patch where the text is new: a compression view of where byte-level models should spend compute
Byte-level language models such as the Byte Latent Transformer (BLT) run their large global model once per patch and start a patch where a small model's next-byte entropy is high.Unfragmenting Nawatl NMT: Single-Variant Specialization for Masewalkopa
In minoritized (low-resource) language technologies, a common response to data sparsity is to aggregate text across multiple varieties and treat the combined data as a single corpus.Explaining the Roles of Experts in Mixture-of-Experts LLMs for Personalization
Personalization of Large Language Models (LLMs) is increasingly important for user-centric applications, yet prior methods mainly adapt dense LLMs, whose polysemantic neurons, each encoding multiple…Intern-Decision: Efficient Multimodal Decision Making with Autoregressive Masked Language Modeling
Interactive agents repeatedly choose among a finite set of actions, labels, or ordinal outcomes.Believed but Not Said: Measuring Hidden Belief Collapse in Multi-Agent LLM Discussions
LLM systems increasingly rely on groups of agents that discuss, critique, and check one another, assuming that peers will catch individual errors.PremiseSQL: Evaluating Premise Critique and Abstention in Text-to-SQL Systems
Recent advances in large language models (LLMs) have substantially improved Text-to-SQL performance, yet existing NL2SQL systems largely assume that user queries are valid and answerable.EMO: A Closed-Loop Test-Time Computation Framework via Alternating Context Optimization and Logits Steering
Test-time context optimization adapts frozen language models through textual feedback, but ordinary conditional generation provides no separate control over how strongly to reinforce the…