RAG’s Receipt Problem: When Correct Answers Don’t Prove Retrieval
Why enterprise RAG evaluation needs both leakage-resistant benchmarks and internal attribution diagnostics before it can claim evidence-grounded answers.
Cognaptus Insights
Industry thoughts, trends, and commentary.
Read the Receipt: Why RAG Should Highlight Before It Answers
A mechanism-first reading of ACL-Verbatim, showing why trustworthy research QA may need extractive evidence spans before generative answers.
Context Is Not a Costume: Why Strong Agents Still Fail on Contact
Two new agent papers show why deployment readiness depends less on generic capability than on explicit adaptation to users, tasks, and shifted environments.
Experience Is Not Memory: Why Learning Agents Need a Better Feedback Loop
A mechanism-first reading of In-context Training, a new framework for testing whether language agents can turn one-off experience into reusable operational improvement.
If Logic Were Enough: Why LLMs Still Miss the Point of Conditionals
A study of conditional reasoning shows why LLMs can pass formal logic tests while still failing at the pragmatic interpretation businesses actually need.
Jailbreak ASR Is Wearing a Costume
A study of LLM jailbreak benchmarks shows why headline attack-success rates can be inflated by stochastic evaluation, judge settings, and undisclosed generation protocols.
Search Me: Why PIPER Makes Tables Findable When Metadata Goes Missing
A comparison-based reading of PIPER, a content-driven approach to tabular dataset search for metadata-poor data ecosystems.
The Confidence Trick: When Long AI Reasoning Arrives Too Early
A mechanism-first reading of premature confidence: why longer reasoning traces can still be post-hoc decoration, and how confidence trajectories may help diagnose and train better LLM reasoning.
Think Longer, Act Worse? What M2A Teaches About Reasoning Agents
A mechanism-first reading of M2A, showing why better reasoning agents need protected action loops, not just longer thought traces.
Energy Bills for Transformers: CEM Makes Layer Design Less Empirical
A mechanism-first reading of Causal Energy Minimization, showing how energy-update logic explains Transformer layer parameterization and where its business relevance begins and ends.