TL;DR for operators An agent can already have the information needed to answer a question and still produce the wrong answer. Diehl and Lim’s Evidence-Inference Reconstruction (EIR)1 addresses that failure mode by storing retrieved source sentences separately from the agent’s evolving interpretations, then generating the scored answer in a fresh call that sees only the original question and accumulated evidence. ...
TL;DR for operators If two research systems start from the same candidate papers, use the same reranking process, have the same ten-paper writing budget, and use the same GPT-4o writer, should their final citations differ much? In Huang et al.’s study1, they do. When the writer also receives structured views that break papers into claims, premises, reasoning steps, conditions, and provenance, citation F1 rises by 5.3 points on ScholarQA-CS and 5.1 points on ScholarQA-Multi, with both precision and recall improving. ...
TL;DR for operators A retrieval system can find a useful first document and still be searching incorrectly afterward. That document may reveal a person, relationship, date, product, or policy that makes the missing evidence much easier to specify than it was from the original request. EviReform1 makes that change explicit: it reads initially retrieved evidence, generates new queries describing what remains unresolved, retrieves against those queries, preserves part of the original-question signal, and only then uses graph connections to consolidate evidence. The paper’s component tests indicate that this query reformulation accounts for most of the improvement; graph propagation adds a smaller, consistent increment. ...
Video is easy to collect and expensive to understand. That is the awkward little truth behind many enterprise “AI video intelligence” projects. A warehouse camera records everything. A body camera records everything. A meeting room system records everything. A field-service headset records everything. Then someone asks a very human question: who handled the device after lunch, what did they say, and was the machine hot when they touched it? ...