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QuantForge is introduced, a PTQ discovery system that records competing explanations, selects controls that distinguish them, and checks that successor code implements the resulting conclusions, improving the discovery of transferable PTQ algorithms by turning controlled evidence into subsequent program changes.
This work introduces ProofLoom, a fully automated LLM-agent system for Proof-Obligation-Driven Theory Construction, a fully automated LLM-agent system for Proof-Obligation-Driven Theory Construction that autonomously constructs the Lean model and supporting theory.
TierKV is presented, a mobile LLM inference framework built on Predictive Multi-Tier Cache Optimization (PMCO), which improves prefill throughput by up to 17.6x over existing mobile LLM frameworks, reduces RAM-resident KV cache by 12.5-34%, thereby enabling substantially longer contexts under the same memory budget, wh...
Many long-horizon LLM deployments face tight prompt budgets: latency, cost, and context limits make full-context prompting impractical as interaction length grows. The key question is then not raw recall alone, but which memory design gives the best quality--token trade-off in the compact-memory regime. We present \tex...
ReasFlow is introduced, an end-to-end autonomous agent system for reasoning-centric scientific discovery that operationalizes a collaborative paradigm where the human expert acts as Principal Investigator while the agent executes rigorous derivations as a capable graduate student.
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