One Agent Is a Bottleneck: When Genomics QA Finally Went Multi-Agent
A mechanism-first reading of GenomAgent: why specialized multi-agent orchestration improved genomics QA accuracy while cutting tool-use cost.
Cognaptus Insights
Industry thoughts, trends, and commentary.
Reasoning or Guessing? When Recursive Models Hit the Wrong Fixed Point
A mechanistic reading of HRM shows why recursive depth can look like reasoning while behaving more like attractor search—and how that changes reliability testing for business AI systems.
When Agents Talk Back: Why AI Collectives Need a Social Theory
A mechanism-first reading of why LLM agent teams cannot be governed by single-agent benchmarks or MARL logic alone.
When Goals Collide: Synthesizing the Best Possible Outcome
How multi-property LTLf synthesis turns impossible all-or-nothing specifications into computable frontiers of guaranteed outcomes.
When Models Know They’re Wrong: Catching Jailbreaks Mid-Sentence
SafeProbing suggests that jailbreak defense may work better when models are monitored during generation, not judged only after the damage is already written.
EvoFSM: Teaching AI Agents to Evolve Without Losing Their Minds
A mechanism-first reading of EvoFSM, a finite-state-machine approach to making self-evolving AI research agents more adaptive without letting them rewrite themselves into chaos.
Knowing Is Not Doing: When LLM Agents Pass the Task but Fail the World
Task2Quiz shows why agent evaluation needs to separate task completion from grounded environment understanding.
Lean LLMs, Heavy Lifting: When Workflows Beat Bigger Models
A case-first look at why structured workflows and data tools, not just larger models, are the real bottleneck-breakers for large-scale optimization modeling.
Seeing Is Thinking: When Multimodal Reasoning Stops Talking and Starts Drawing
A mechanism-first reading of Omni-R1, a paper that turns multimodal reasoning from text-only explanation into interleaved visual action.
When Agents Learn Without Learning: Test-Time Reinforcement Comes of Age
MATTRL shows how multi-agent systems can improve at inference time by turning past collaboration into credit-assigned, retrievable operational memory.