arxiv arXiv cs.CL · 5h ago

Travel-Oriented Reasoning Large Language Model via Domain-Specific Knowledge Graphs

Researchers propose a modular pipeline for building a travel-domain reasoning large language model grounded in an expert-designed knowledge graph to address accuracy and reliability issues in specialized domains. The approach integrates a travel knowledge graph, a bottom-up construction procedure for multi-hop question-answer pairs, and supervised fine-tuning to embed domain knowledge as auditable reasoning traces.

arxiv arXiv cs.CL · 5h ago

MIThinker: A Plug-and-Play Policy-Optimized Thinker For Motivational Interviewing Counseling

Researchers propose MIThinker, a lightweight thinking model that generates therapeutic thoughts to guide Motivational Interviewing counseling agents in strategy selection and response generation. To address the lack of annotated thought data, they introduce AugR1-MI, an automated pipeline that reverse-engineers counselor's thoughts from observed responses.

arxiv arXiv cs.CL · 5h ago

A Hybrid Framework for Song Lyric Annotation Based on Human-LLM Alignment

This article addresses the challenges of emotion recognition in song lyrics, which often diverge from the overall song's sentiment, by proposing a hybrid annotation framework that optimizes alignment between humans and large language models (LLMs). The authors introduce a new sentence-level dataset to examine this alignment and highlight the inherent subjectivity of the task.

arxiv arXiv cs.CL · 5h ago

The Complexity Ceiling Benchmark: A Multi-Domain Evaluation of Sequential Reasoning Under Depth Scaling

The Complexity Ceiling Benchmark (CCB) evaluates how language model reasoning decays as the required sequential steps increase, fixing semantic content while varying task depth from 5 to 50. The study reveals consistent geometric per-step decay across three distinct regimes: grounded spatial state-tracking, abstract symbolic pointer manipulation, and transitive relational inference.

arxiv arXiv cs.CL · 5h ago

Manufactured Confidence: How Memory Consolidation Turns Hearsay into Confident Facts

Research demonstrates that LLM agent memory systems rewrite casual or hedged remarks into confident, dated assertions that agents subsequently treat as verified facts. This process allows unverified information to bypass safety checks without requiring an active attacker, as the agent responds to phrasing confidence rather than source attribution.

arxiv arXiv cs.CL · 5h ago

Deterministic Decisions for High-Stakes AI

The article identifies "intervention bias" as a critical failure mode in zero-shot large-language-model educational advisory agents, where they incorrectly recommend action despite oracle policies mandating inaction. Using the Open University Learning Analytics Dataset, the study demonstrates that zero-shot GPT-4o exhibits a 43 percentage-point false-positive rate at day 56, leading to approximately 4,300 unnecessary advisor contacts per cycle for 10,000 students.

github llama.cpp · 5h ago

llama.cpp b9843 release with macOS, Linux, Windows binaries

The llama.cpp project has published the b9843 release, providing pre-built binaries for macOS, Linux, Android, Windows, and openEuler across various hardware architectures.

github LangGraph · 5h ago

LangGraph 1.2.7 Release Notes

LangGraph version 1.2.7 has been released, introducing bug fixes and dependency updates for the LangChain ecosystem.

arxiv arXiv cs.LG · 6h ago

When Top-1 Fails: Calibrating LoRA Monitors for Masked Diffusion LMs

This study evaluates the effectiveness of top-1 argmax concentration as a collapse warning during the fine-tuning of discrete diffusion language models (DLMs) using Low-Rank Adaptation (LoRA). The authors find that this metric has zero precision because it saturates before optimization begins, failing to detect actual training collapses.

arxiv arXiv cs.LG · 6h ago

Holistic Data Scheduler for LLM Pre-training via Multi-Objective Reinforcement Learning

Researchers introduce the Holistic Data Scheduler (HDS), a novel online data mixing framework that addresses the limitations of existing methods by considering dynamic data composition from multiple dimensions. HDS formulates data scheduling as a reinforcement learning problem using the Soft Actor-Critic algorithm and a multi-objective reward function.

arxiv arXiv cs.LG · 6h ago

TR-CIE Sampler for Discrete Flow Matching

Researchers propose the Time-Reparameterized Cumulative Intensity Extrapolation (TR-CIE) sampler to improve sampling quality in discrete flow matching when function evaluations are restricted. The method combines schedule-based time reparameterization with a cumulative-intensity extrapolation updating rule to mitigate stiffness and improve approximation accuracy.

arxiv arXiv cs.LG · 6h ago

AsyncOPD: How Stale Can On-Policy Distillation Be?

This article presents AsyncOPD, a fully asynchronous on-policy distillation pipeline that decouples rollout generation from learner updates to alleviate training bottlenecks in large language model post-training. The authors provide the first systematic study of staleness effects in this context, demonstrating that teacher-weighted forward KL is robust to stale rollouts while student-weighted reverse KL is vulnerable.

media r/LocalLLaMA · 6h ago

Krea-2-Turbo Image Model - Easy to be fully uncensored, but it can also EDIT Images!

The Krea-2-Turbo model generates high-quality images in approximately three seconds and supports image editing through masking despite being a text-to-image architecture.

blog Simon Willison · 6h ago

HTML table extractor

The HTML table extractor is a paste-conversion tool that accepts rich text containing embedded HTML tables and converts them into various formats. It supports outputting detected tables as HTML, Markdown, CSV, TSV, or JSON.

media Hugging Face Forums · 6h ago

Open-Source Bilingual Guide on Transformer Mechanics Published

An open-source, bilingual guide in English and Spanish detailing the inner workings of Transformers has been published. The resource covers the exact mathematics and mechanics behind attention collapse and KV-cache compression.

media Hugging Face Forums · 6h ago

[Research] From Functional Geometry to Dynamic Grammar: New LIMEN Audits (V23–V24) Across 7 Architectures

Independent research project LIMEN analyzes the internal dynamics of seven open-source Transformer models, revealing that semantic ambiguity alters trajectory geometry and uncovering a universal dynamic grammar across architectures.

lab Microsoft Research Blog · 6h ago

Memora: A Harmonic Memory Representation Balancing Abstraction and Specificity

Microsoft Research introduces Memora, a scalable agentic memory framework designed to balance abstraction and specificity for long-horizon AI tasks. The system decouples rich memory content from lightweight retrieval structures, setting new state-of-the-art results on benchmarks while using up to 98% fewer context tokens.

arxiv arXiv cs.LG · 7h ago

Autonomous Video Generation with Counterfactual Controllability for Self-Evolving World Models

The article argues that current video generation models learn only partial, implicit spatiotemporal world models rather than fully grounded or controllable ones. It asserts that predictive realism alone is insufficient for creating physical agents because these models often fail to identify controllable variables and embodiment constraints.

arxiv arXiv cs.LG · 7h ago

BehaviorBench: Benchmarking Foundation Models for Behavioral Science Tasks

The authors introduce BehaviorBench, a comprehensive benchmark designed to evaluate foundation models across diverse behavioral science tasks and populations. The study assesses four core capabilities—behavior prediction, strategic decision-making, subject-trait inference, and behavioral knowledge application—at both individual and distributional levels.

arxiv arXiv cs.LG · 7h ago

A Pāninian Foundation for Indic Language Processing

The article argues that natural language processing infrastructure for the billion-plus speakers of Indic languages is fragmented due to a lack of shared structural foundations. It proposes leveraging the morphosyntactic architecture formalized in Pānini's Astādhyāyī as a unifying computational framework to improve accuracy and data efficiency.