Researchers present RaDaR, an open-source 32B parameter reasoning large language model designed to accelerate the diagnosis of rare diseases by addressing challenges in clinical deployability and data scarcity. The model was trained on nearly 50,000 public cases and over 100,000 synthetic cases, demonstrating superior performance across benchmarks and external validation centers.
The authors propose a reinforcement learning fine-tuning framework that utilizes autonomous vision-language evaluation as a scalable supervision signal for GUI agents, eliminating the need for manual labels or task-specific heuristics. By treating evaluator feedback as a noisy binary reward channel and deriving a noise-corrected estimator for Proximal Policy Optimization, the method addresses the difficulty of obtaining machine-readable rewards in open-ended desktop environments.
The authors present AdversaBench, an end-to-end red-teaming pipeline that generates hard inputs for large language models using five structured mutation operators and confirms failures through a three-judge panel with a meta-judge tiebreaker.
A lawsuit has been filed in the United States against major memory chip manufacturers Samsung, SK hynix, and Micron regarding allegations of price fixing.
DeepReinforce has released Ornith-1.0, an open-weight model licensed under MIT that achieves state-of-the-art performance among open-source models of comparable size on coding benchmarks. The model is built upon pretrained Gemma 4 and Qwen 3.5 foundations and includes variants with 9B Dense, 31B Dense, 35B MoE, and 397B MoE parameter counts.
A researcher submitting their first paper to arXiv reports that the manuscript has been under moderator review for two months despite passing automatic qualification checks. The author inquires whether this delay is normal and asks for advice on whether to resubmit or continue waiting.
The llama.cpp b9842 release introduces a change to deduplicate preset and cached model entries in the /v1/models endpoint. This update is signed off by Adrien Gallouët from Hugging Face.
This research investigates the use of large language models to detect scam phone calls in Turkish, a low-resource language where annotated data is scarce. The study introduces the first public multi-modal dataset containing 100 aligned audio-transcript pairs of scam and benign conversations.
This paper formalizes the fleet-memory problem in multi-agent LLM environments, identifying four foundational failure modes: unauthorized leakage, stale propagation, contradiction persistence, and provenance collapse. To address these issues, the authors define explicit systems-level primitives including scoped retrieval, temporal supersession, provenance tracking, and policy-governed memory propagation.
This research tests whether Benjamin Graham's classic value investing rules can act as a mathematical filter to prevent complex machine learning models from memorizing market noise. The study compares pure Graham rules, modern factors, and a combination of both against XGBoost and AutoGluon models using 20 years of S&P 500 data.
A study investigates the impact of overrefusal on small, on-device large language models when processing legal prompts, finding that authority-style prefixes systematically increase refusal rates by 2 to 20 times compared to a no-prefix baseline. While role-play jailbreak prefixes showed mixed effects across different models, the results indicate that these small LLMs are unstable under contextual framings typical of real institutional users.
This paper introduces ASALT, a method for lateral transfer learning in multi-agent reinforcement learning that accommodates mismatched state-space dimensionalities between source and target domains. The approach uses observation-level and state-level adapters to map inputs into a shared embedding space, enabling effective knowledge transfer across heterogeneous environments.
The author argues that upgrading from a single to dual GPU offers greater benefits through parallel processing rather than enabling the use of larger, higher-quality model quantizations. For coding tasks, the quality difference between Q4 and Q6/Q8 quantizations is minimal, making increased context window and throughput more valuable.
A Reddit user shared an image titled "Effect of GLM 5.2 !!" in the r/LocalLLaMA subreddit.
The author argues that the open-source community should prioritize building a massive, high-quality pre-training dataset rather than attempting to coordinate decentralized LLM training across home GPUs. This shift is presented as a more practical and immediate response to recent government bans on commercial frontier models and a scarcity of small-to-medium open-weight releases.
Bolt Graphics is developing a GPU that includes two DDR5 SODIMM slots for overflow memory, aiming for full production by Christmas 2027. The company has working prototypes and targets creators as its initial audience.
This study proposes a probabilistic framework for longitudinal modeling of Alzheimer's disease progression that combines ordinal diagnosis prediction, multi-horizon trajectory generation, and decomposed uncertainty estimation. The approach utilizes a Temporal Fusion Transformer encoder and an autoregressive Mixture Density Network to generate five-year probabilistic trajectories while quantifying both aleatoric and epistemic uncertainty.
The paper introduces ScaleToT, a method that learns structured reasoning from a small subset of users and extends it to billions of low-activity users with sparse profiles. It combines a bounded entropy-guided Tree-of-Thought refinement with supervised fine-tuning and reward policy optimization to transfer reasoning capabilities without full LLM inference.
This article addresses query abstraction in ontology-based data access (OBDA) by translating data queries to the ontology layer using existential rules and certain answer semantics.
This paper investigates the challenges of Competency Question (CQ) verification, a process where ontologies are evaluated against natural language questions to ensure proper modeling. The authors analyze why CQs become difficult and how an LLM assistant can support users during this evaluation.