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.
Cognaptus Insights
Industry thoughts, trends, and commentary.
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.
Rank and File: MatryoshkaLoRA Turns One Adapter into Many
A mechanism-first reading of MatryoshkaLoRA, showing why one diagonal training weight can make LoRA adapters usable across multiple deployment ranks.
The Edge Case for LLM Routing: Why Cheap Local Inference Needs a Risk Gate
CR2 shows why mobile-edge LLM routing is not just model selection with a smaller model attached, but a two-stage deployment problem where local confidence, wireless cost, and risk control must be designed together.