AI

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A Two-Stage Architecture for NDA Analysis: LLM-based Segmentation and Transformer-based Clause Classification

Researchers developed a two-stage AI architecture using LLaMA-3.1-8B-Instruct and Legal-Roberta-Large models to automate the analysis of Non-Disclosure Agreements (NDAs). The system achieved high accuracy with ROUGE F1 of 0.95 for document segmentation and weighted F1 of 0.85 for clause classification, demonstrating potential for automating legal document analysis.

HEAL: Hindsight Entropy-Assisted Learning for Reasoning Distillation

Researchers introduce HEAL (Hindsight Entropy-Assisted Learning), a new framework for distilling reasoning capabilities from large AI models into smaller ones. The method overcomes traditional limitations by using three core modules to bridge reasoning gaps and significantly outperforms standard distillation techniques.

Causally Grounded Mechanistic Interpretability for LLMs with Faithful Natural-Language Explanations

Researchers developed a pipeline to translate AI model internal mechanisms into human-understandable explanations, testing on GPT-2 Small. The study identified six attention heads responsible for 61.4% of model performance on a specific task, with LLM-generated explanations outperforming template-based approaches by 64%.

The System Hallucination Scale (SHS): A Minimal yet Effective Human-Centered Instrument for Evaluating Hallucination-Related Behavior in Large Language Models

Researchers have developed the System Hallucination Scale (SHS), a human-centered tool for evaluating hallucination behavior in large language models. The instrument showed strong statistical validity in testing with 210 participants and provides a practical method for assessing AI model reliability from a user perspective.

One Model, Many Skills: Parameter-Efficient Fine-Tuning for Multitask Code Analysis

Researchers conducted the first comprehensive evaluation of parameter-efficient fine-tuning (PEFT) for multi-task code analysis, showing that a single PEFT module can match full fine-tuning performance while reducing computational costs by up to 85%. The study found that even 1B-parameter models with multi-task PEFT outperform large general-purpose LLMs like DeepSeek and CodeLlama on code analysis tasks.

FAME: Formal Abstract Minimal Explanation for Neural Networks

Researchers introduce FAME (Formal Abstract Minimal Explanations), a new method for explaining neural network decisions that scales to large networks while producing smaller explanations. The approach uses abstract interpretation and dedicated perturbation domains to eliminate irrelevant features and converge to minimal explanations more efficiently than existing methods.

Resource-constrained Amazons chess decision framework integrating large language models and graph attention

Researchers developed a lightweight AI framework for the Game of the Amazons that combines graph attention networks with large language models, achieving 15-56% improvement in decision accuracy while using minimal computational resources. The hybrid approach demonstrates weak-to-strong generalization by leveraging GPT-4o-mini for synthetic training data and graph-based learning for structural reasoning.

Adaptive RAN Slicing Control via Reward-Free Self-Finetuning Agents

Researchers propose a novel self-finetuning framework for AI agents that enables continuous learning without handcrafted rewards, demonstrating superior performance in dynamic Radio Access Network slicing tasks. The approach uses bi-perspective reflection to generate autonomous feedback and distill long-term experiences into model parameters, outperforming traditional reinforcement learning methods.

Trajectory-Informed Memory Generation for Self-Improving Agent Systems

Researchers introduce a new framework for AI agent systems that automatically extracts learnings from execution trajectories to improve future performance. The system uses four components including trajectory analysis and contextual memory retrieval, achieving up to 14.3 percentage point improvements in task completion on benchmarks.

Emulating Clinician Cognition via Self-Evolving Deep Clinical Research

Researchers developed DxEvolve, a self-evolving AI diagnostic system that mimics clinical reasoning through interactive workflows and continuous learning. The system achieved 90.4% diagnostic accuracy on benchmarks, comparable to human clinicians at 88.8%, and showed significant improvements over traditional AI models.

Nurture-First Agent Development: Building Domain-Expert AI Agents Through Conversational Knowledge Crystallization

Researchers propose Nurture-First Development (NFD), a new paradigm for building domain-expert AI agents through progressive growth via conversational interaction rather than traditional code-first or prompt-first approaches. The method uses a Knowledge Crystallization Cycle to convert operational dialogue into structured knowledge assets, demonstrated through a financial research agent case study.

Burn-After-Use for Preventing Data Leakage through a Secure Multi-Tenant Architecture in Enterprise LLM

Verbalizing LLM's Higher-order Uncertainty via Imprecise Probabilities

Researchers propose new uncertainty elicitation techniques for large language models using imprecise probabilities framework to better capture higher-order uncertainty. The approach addresses systematic failures in ambiguous question-answering and self-reflection by quantifying both first-order uncertainty over responses and second-order uncertainty about the probability model itself.

Dynamics-Predictive Sampling for Active RL Finetuning of Large Reasoning Models

Researchers propose Dynamics-Predictive Sampling (DPS), a new method that improves reinforcement learning finetuning of large language models by predicting which training prompts will be most informative without expensive computational rollouts. The technique models each prompt's learning progress as a dynamical system and uses Bayesian inference to select better training data, reducing computational overhead while achieving superior reasoning performance.

Towards Cold-Start Drafting and Continual Refining: A Value-Driven Memory Approach with Application to NPU Kernel Synthesis

Researchers introduce EvoKernel, a self-evolving AI framework that addresses the 'Data Wall' problem in deploying Large Language Models for kernel synthesis on data-scarce hardware platforms like NPUs. The system uses memory-based reinforcement learning to improve correctness from 11% to 83% and achieves 3.60x speedup through iterative refinement.

LookaheadKV: Fast and Accurate KV Cache Eviction by Glimpsing into the Future without Generation

Researchers have developed LookaheadKV, a new framework that significantly improves memory efficiency in large language models by intelligently evicting less important cached data. The method achieves superior accuracy while reducing computational costs by up to 14.5x compared to existing approaches, making long-context AI tasks more practical.

When Fine-Tuning Fails and when it Generalises: Role of Data Diversity and Mixed Training in LLM-based TTS

Research demonstrates that LoRA fine-tuning of large language models significantly improves text-to-speech systems, achieving up to 0.42 DNS-MOS gains and 34% SNR improvements when training data has sufficient acoustic diversity. The study establishes LoRA as an effective mechanism for speaker adaptation in compact LLM-based TTS systems, outperforming frozen base models across perceptual quality, speaker fidelity, and signal quality metrics.

RandMark: On Random Watermarking of Visual Foundation Models

Researchers propose RandMark, a new method for watermarking visual foundation models to protect intellectual property rights. The approach uses a small encoder-decoder network to embed random digital watermarks into internal representations, enabling ownership verification with low false detection rates.

CUPID: A Plug-in Framework for Joint Aleatoric and Epistemic Uncertainty Estimation with a Single Model

Researchers introduce CUPID, a plug-in framework that estimates both aleatoric and epistemic uncertainty in deep learning models without requiring model retraining. The modular approach can be inserted into any layer of pretrained networks and provides interpretable uncertainty analysis for high-stakes AI applications.