arXiv AI
Terminal Symmetry as a Carrier of Asymmetric Process Knowledge: Statewise Refinement for Anytime Verified Construction
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arXiv AI
Aug 13
Terminal Symmetry as a Decision Resource: Statewise Refinement for Anytime Verified Construction
arXiv:2608. 11318v1 Announce Type: cross Abstract: Many sequential construction tasks exhibit exact symmetry at completion while their execution remains directed and history-dependent.
arXiv AI
Aug 20
Governance Records as Supervision: Verifier-Selected Self-Training for Structured Workflow Repair
The study demonstrates that governance records—structured logs linking task contracts, model attempts, verifier decisions, and outputs—can serve as effective supervision for bounded AI models. Using a verifier-selected self‑training approach, the authors show that a Qwen3‑14B model trained on plans accepted by an independent VAL verifier achieved significant gains in plan acceptance across numerous PlanBench replanning cases, outperforming other selection strategies. The results highlight the feasibility of one‑shot execution and cumulative learning without relying on oracle targets or stronger teachers.
arXiv Machine Learning
Aug 19
Elimination Geometry
The monograph introduces Elimination Geometry (EG), a typed, native‑loss, audit‑oriented framework that investigates when locally optimal objects can be realized by a shared deployment rule. EG examines how elimination and compression can erase distinctions needed for prediction, inference, control, or representation, and it separates local solvability, global realizability, and finite‑sample certifiability. The work synthesizes tools from geometry, optimization, information theory, statistics, and machine learning to address regular, coordination, singular, compositional, and resource‑limited mechanisms, and demonstrates applications in sparse model selection, distribution‑free prediction, observational treatment policies, routed expert and retrieval systems, and learned score fields.
arXiv AI
Jul 21
Constrained Path Reasoning: Measuring When Committed Stages Earn Their Cost
arXiv:2607. 17240v1 Announce Type: new Abstract: When does a committed intermediate stage in an LLM reasoning pipeline earn its cost?
arXiv AI
Jun 24
2.5-D Decomposition for LLM-Based Spatial Construction
arXiv:2605. 07066v3 Announce Type: replace Abstract: Autonomous systems that build structures from natural-language instructions need reliable spatial reasoning, yet large language models (LLMs) make systematic coordinate errors when generating three-dimensional block placements.
arXiv AI
Jun 19
FAPO: Fully Autonomous Prompt Optimization of Multi-Step LLM Pipelines
arXiv:2606. 19605v1 Announce Type: cross Abstract: Multi-step LLM pipelines fail through interactions among retrieval, reasoning, and formatting steps, so prompt-only optimization can miss bottlenecks in the chain.