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.
This paper introduces a categorical account of infinitesimal causality in Frobenius Markov categories equipped with tangent-bundle semantics. It defines causal sufficiency through the compatibility of two distinct Frobenius structures: one encoding classical variable operations and another representing geometric integrability.
The authors introduce Themis, an XAI-enabled testing and evaluation framework that combines transparency through explainability with alignment via human feedback for safe Reinforcement Learning systems.
The authors propose a multi-agent framework that sanitizes retrieved content in Retrieval-Augmented Generation (RAG) systems through semantic rewriting to prevent privacy leakage from malicious prompts. By employing three specialized agents for privacy extraction, semantic analysis, and reconstruction, the approach removes sensitive identifiers while preserving the core meaning of the text.
The article introduces SAFARI, a framework designed to diagnose failures in autonomous agents by replacing linear context loading with a tool-augmented diagnostic loop. This approach decouples diagnostic accuracy from architectural context limits by using specialized tools and short-term memory to analyze trajectory segments.
This article examines how intentional, pluralistic design choices in AI-enabled digital platforms can produce visualizations that emphasize nuance and intergroup commonalities, thereby reducing political polarization. It highlights a specific deliberative technology initiative that maps high-dimensional opinion spaces to reveal areas of both consensus and dissensus among diverse populations.
JetBrains has open-sourced the Mellum2 models, a series of 12B-2.5A LLMs trained from scratch to target fast inference on H100/H200 hardware as well as local deployments.
Researchers propose CineCap, a framework that combines structured reasoning with spatio-temporal anchors and reinforcement learning to improve cinematographic video captioning. The method grounds professional film-language descriptions in explicit visual evidence while balancing descriptive completeness and factual correctness.