Model releases, architecture work and prompting research on large language models — from frontier-lab announcements to the arXiv papers behind them.
This paper studies \textbf{thinking--answer consistency} in vision-language models. We focus on Visual Intention Grounding, where a model infers a target object based on a human intention query and pr...
Current image-to-video models achieve visual realism and physical plausibility, but reasoning about mental states remains unexplored. Actions are driven by belief, desire, and perception, requiring in...
Can structured text replace vision for diagram reasoning? A wrong answer after textualization can arise because the representation omits information the question needs, or because the solver fails to...
Runtime error healing lets a crashed program continue by generating code that repairs its live runtime state. Recent work shows that LLMs can generate such healing code, but it is evaluated only on sm...
As new evidence arrives, a sequence model must update what it remembers and how memory influences predictions. While Transformers incur computation and cache costs scaling with context length, fixed-s...
Legal language models require reward signals that capture not only answer correctness but also the multidimensional quality of legal responses. Existing reward methods, however, often rely on coarse-g...
A large language model (LLM) can rate depression severity directly from a social media post or mark which clinical criteria the post shows and let code turn the count into a label. The latter is easie...
CLIP is a powerful vision-language model, but it was not designed for fine-grained defect localization; CLIP-based anomaly detectors therefore adapt it with prompts or lightweight modules to increase...
Modern AI coding-agent harnesses (Claude Code, Codex CLI, Cursor) rest their security boundary on a largely unexamined assumption: that the action A a human approves is the same action A' the harness...
Coding agents powered by large language models (LLMs) have shown remarkable abilities to autonomously reason about and achieve goals in the digital world. However, bringing this success to the physica...
Demographic fairness gaps in automatic speech recognition are almost always reported from a single training run. We fine-tune the Q-former projector and LoRA adapters of a speech LLM at five audio com...
Large language model (LLM) agents could help security operations centers (SOCs) investigate advanced persistent threats (APTs) by turning weak leads into evidence for intrusion scoping and response. Y...
Self-consistency samples many reasoning trajectories and aggregates their final answers, treating the LLM as a black box that returns one answer per trajectory. Yet the final answer of each trajectory...
arXiv:2609.37105v1 Announce Type: cross Abstract: Language model agents can be improved by updating their model weights or refining the harness that guides task execution. These components are couple...
arXiv:2609.32490v2 Announce Type: replace Abstract: LLM-based multi-agent systems (MASs) have shown strong potential for solving complex tasks, but most assume that task requirements are sufficiently...
The study demonstrates that large language models (LLMs) can produce highly reliable labels under a single experimental setup, yet their outputs vary significantly when researchers alter task designs or model choices. By evaluating seven LLMs across 12 task designs and 3,000 tweets for offensive language and hate speech, the authors found that agreement dropped from a median Fleiss' κ of 0.91 to a median Cohen's κ of 0.76 when task designs changed. This design sensitivity inflates prevalence estimates by up to 110.6 times compared to sampling variance alone, and confidence scores fail to mitigate the issue.
The paper investigates how large language models (LLMs) handle multi‑valued relations when used as knowledge bases. It identifies a "canonical order problem," where LLMs internally organize such relations according to a fixed ordering (e.g., alphabetical or chronological). The authors analyze the generation process in three phases—retrieval, internal sorting, and selection—and show that deviating from the internal canonical order reduces the reliability of LLMs in producing complete sets for multi‑valued relations.
The paper investigates whether large language models (LLMs) that store persistent memories can truly forget information upon user request. It shows that existing LLMs cannot delete such memories even when they claim to have forgotten them, and that simply removing matching messages is ineffective due to message dependencies. The authors introduce DeLLM, a framework that builds relevant context dynamically and uses a provenance graph to identify which messages must be removed, achieving a high deletion rate while preserving utility.
The paper introduces a holistic framework for large language model self‑evolution that uses learnable information gain to assess the novelty of each training round. Information gain is theoretically linked to the Kullback‑Leibler divergence and entropy change between successive data distributions, and practically estimated by fitting a small language model and scoring new data with negative log‑likelihood. The proposed ATRI method reweights samples within a round and stops training across rounds when information gain is low, and experiments on popular datasets show its effectiveness.
DirectUV is an image-conditioned UV texture diffusion framework that generates high-quality textures directly in UV space using a pretrained image VAE and a Diffusion Transformer. It introduces Surface-Aware Positional Encoding (SAPE), which replaces standard 2D-grid positional encoding with encodings derived from 3D surface coordinates, enabling attention to operate based on surface proximity rather than UV-grid distance. A multi-level extension assigns different attention heads to progressively finer subdivisions of the UV patch, allowing the model to reason about surface structure at multiple granularities, resulting in sharper and more globally consistent textures, especially in occluded and view-unseen regions.