#machine-learning
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WorldClaw: Scaling Agentic 3D Open-World Generation
WorldClaw introduces an agentic framework for generating complex, large-scale 3D open worlds, moving beyond static scene generation toward autonomous, scalable environment creation.
TriQua: A New Framework for Factuality Evaluation in LLMs
TriQua addresses the trade-off between granular fact-checking and global context by decomposing evaluation into three distinct dimensions to improve accuracy in LLM output verification.
Solving Misalignment in Multi-Turn AI Agent Guidance
This paper addresses the failure modes of privileged guidance in multi-turn agents, proposing state-matched routing and contextualized self-distillation to prevent performance degradation when teacher models provide misaligned instructions.
SkillTrace: Auditing Provenance in LLM-Agent Skill Reuse
SkillTrace provides a framework for auditing the provenance of skills reused by LLM agents, ensuring transparency and accountability when agents leverage previously learned capabilities across multiple execution traces.
Measuring Global Workspace Dynamics in LLMs with the Ignition Index
The Ignition Index provides a quantitative framework to measure Global Workspace Theory (GWT) dynamics in LLMs, offering a new way to evaluate model reasoning and information integration.
Woodpecker Distillation: Using Weak Models to Debug Strong LLMs
Woodpecker Distillation improves LLM reasoning by using smaller, 'weaker' models to identify and diagnose logic errors in the outputs of larger, more powerful models, enabling iterative refinement without requiring massive compute for every step.
Structure-Aware Shapley Valuation for AI Agent Skills
This paper introduces a method to quantify the individual contribution of specific skills within an AI agent's repertoire by accounting for the hierarchical and dependency structures between them.
The RAIL Principles for Neurosymbolic AI
The RAIL framework provides a structured approach to neurosymbolic AI by integrating symbolic reasoning, formal assurances, intuitive human-AI interfacing, and continuous learning to overcome the limitations of pure neural models.
Adversarially Robust Abductive Fusion for Perception Models
This paper introduces a framework for combining pre-trained transformer perception models using abductive reasoning to improve robustness against adversarial attacks.
SafeCommit: Certifying Safety for Memory-Grounded AI Agents
SafeCommit is a framework that introduces a certification mechanism to determine when memory-grounded AI agents can safely execute actions based on their internal state, reducing the risk of hallucinated or harmful operations.
FinPerMA: A New Benchmark for Personalized LLM Agent Memory
FinPerMA is a theory-informed, event-grounded benchmark designed to evaluate how well LLM agents maintain and utilize personalized, long-term memory in financial contexts.
Understanding AI Model Collapse and Data Degradation
Model collapse occurs when AI models are trained on synthetic data, leading to the loss of rare information and a drift away from reality. Preventing this requires maintaining human-generated data, rigorous data provenance, and external grounding via RAG.
IBM TechnologyDiffImaginE: Using Diffusion Models for Entity Type Verification
DiffImaginE leverages diffusion models to verify entity types by generating visual representations, providing a novel bridge between textual entity classification and generative AI.
LoCA: Efficient Forward-Only LLM Tuning via Local Credit Assignment
LoCA enables LLM fine-tuning without backpropagation by using one-shot calibration and local credit assignment, significantly reducing memory overhead and computational complexity.
Addressing the Missing Benchmarks Layer in AI Evaluation
Current AI evaluation suffers from a lack of a standardized 'benchmarks layer,' leading to fragmented and unreliable performance metrics. The paper proposes a structural solution to unify how models are tested and compared.
Information Boundaries for Group-Robust LLM Pruning
Standard LLM pruning metrics often fail to account for group-level performance disparities; this research proposes information-theoretic boundaries to ensure robustness across diverse data subgroups.
The Missing Data Layer in AI Systems
Current AI architectures lack a dedicated, standardized data layer, leading to fragmented pipelines; the proposed solution involves a unified abstraction for data management that bridges the gap between raw storage and model inference.
Escaping LLM Homogeneity with Meta-Persona Anchoring
To combat output uniformity in LLMs, use Meta-Persona Anchoring to define high-level cognitive constraints and Sequential Temperature Scaling to manage creative variance across multi-step reasoning chains.
Large Database Models: Bringing AI Directly to SQL Data
Large Database Models (LDMs) allow AI to perform semantic analysis directly within relational databases, eliminating the need to move data to external platforms for machine learning and enabling SQL-based similarity searches.
IBM TechnologySciToolAgent-Evo: Ontology-Driven Self-Evolving AI Agents
SciToolAgent-Evo addresses the limitations of static AI agents in scientific research by using an ontology-aware framework that allows agents to autonomously discover, evaluate, and integrate new tools in open-world environments.
Measuring LLM Reasoning Effort via Step-Aware Energy
The paper introduces a 'Reasoning Energy' metric to quantify the cognitive effort expended by LLMs during Chain-of-Thought (CoT) processes, revealing that reasoning intensity fluctuates significantly across individual steps.
Multi-Agent Planning with STL-GO
STL-GO is a formal methods approach for multi-agent path planning that enforces complex spatio-temporal and topological constraints using Signal Temporal Logic (STL) and gradient-based optimization.
ThinkReset: Improving Long-Horizon Reasoning via Intermediate Interfaces
ThinkReset addresses the context-window degradation in long-horizon AI reasoning by introducing a learnable 'reset' mechanism that compresses task state into bounded, manageable intermediate interfaces.
AlphaSchema: Semantic Frameworks for LLM-Driven Alpha Mining
AlphaSchema introduces a structured semantic framework to improve how LLMs generate and evaluate quantitative trading signals (alphas), moving beyond unstructured prompt engineering to systematic search spaces.
Automating Ascend C Operator Generation with AgenticCANN
AgenticCANN leverages a knowledge-augmented agentic evolution framework to automate the complex, manual process of writing high-performance Ascend C operators for AI hardware.
Mitigating Skill Overfitting in AI Self-Evolution
Self-evolving AI models often suffer from 'skill overfitting,' where performance on specific tasks improves at the expense of general capabilities. The authors propose a constrained exploration-exploitation framework to balance task-specific refinement with broader model robustness.
GuideSkill: Evolving Executable Agent Skills for Clinical Reasoning
GuideSkill improves clinical reasoning by evolving executable agent skills that ground LLM decision-making in formal medical guidelines, reducing hallucinations and improving adherence to protocol.
MultivationBench: Evaluating Multimodal Sequential Motivation Reasoning
MultivationBench is a new benchmark designed to test how well multimodal AI models understand the underlying motivations behind sequences of actions in visual and textual contexts.
Why AI Evaluation Scores Decay Over Time
AI evaluation scores are not static truths but perishable knowledge claims that degrade as models evolve, data distributions shift, and benchmarks become contaminated.
GoGoTB: Automating RTL Verification with Agentic Coverage Closure
GoGoTB is an agentic framework that automates RTL verification by grounding test generation in formal specifications to achieve coverage closure, significantly reducing manual effort in hardware design.
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