Verification

Why this matters now Large language models have grown remarkably persuasive—but not necessarily reliable. They often arrive at correct answers through logically unsound reasoning, a phenomenon both amusing in games and catastrophic in legal, biomedical, or policy contexts. The research paper VeriCoT: Neuro-Symbolic Chain-of-Thought Validation via Logical Consistency Checks proposes a decisive step toward addressing that flaw: a hybrid system where symbolic logic checks the reasoning of a neural model, not just its answers. ...

TL;DR Most “agent memory” is a junk drawer: it grows fast, gets noisy, and slows everything down. SEDM (Self‑Evolving Distributed Memory) proposes an auditable, efficiency‑first overhaul. It verifies each candidate memory by replaying the exact run in a Self‑Contained Execution Context (SCEC), assigns an initial utility‑aligned weight, and then self‑schedules what to retrieve next. The result: higher task accuracy with fewer tokens versus strong memory baselines on FEVER and HotpotQA. ...