Atom by Atom, Better Research: How Fine-Grained Rewards Make Agentic Search Smarter
Atom-Searcher shows why research agents may need rewards for intermediate research behaviour, not just final-answer correctness.
Cognaptus Insights
Industry thoughts, trends, and commentary.
Crystal Ball, Meet Cron Job: What FutureX Reveals About ‘Live’ Forecasting Agents
FutureX shows that forecasting agents should be judged as live operating systems, not static models with better vibes.
Forgetting by Design: Turning GDPR into a Systems Problem for LLMs
TL;DR for operators A deletion request is not a prompt. It is not a “please forget” instruction, a fine-tuning vibe, or a compliance-flavoured model apology. The useful idea in Unlearning at Scale: Implementing the Right to be Forgotten in Large Language Models is much less mystical: make training reproducible enough that deletion can be executed like systems recovery.1 The paper treats training as a deterministic program, logs the minimal control inputs needed to replay that program, and then removes the requested data during replay. Under strict preconditions, the resulting parameters are bit-identical, in the training dtype, to the model that would have been produced if the forgotten examples had never been included. ...
Precepts over Predictions: Can LLMs Play Socrates?
AMAeval shows why moral AI evaluation should test how models derive situation-specific rules from values, not merely whether they reach acceptable verdicts.
Survival of the Fittest Prompt: When LLM Agents Choose Life Over the Mission
A practical reading of what Sugarscape-style survival experiments reveal about LLM agents, task reliability, and operational control.
Agents on the Wire: Protocols, Memory, and Guardrails for Real-World Agentic AI
A comparison-based reading of agentic AI frameworks that turns protocol, memory, guardrail, and service-computing differences into practical architecture choices.
Bias in the Warehouse: What AIM-Bench Reveals About Agentic LLMs
AIM-Bench shows that LLM inventory agents do not fail like generic chatbots; they fail like operational decision-makers with measurable biases.
Consent, Coaxing, and Countermoves: Simulating Privacy Attacks on LLM Agents
A search-based privacy red-teaming framework shows how agent-agent attacks evolve from blunt requests into forged consent and impersonation—and why static privacy prompts are not enough.
Keys to the Kingdom: How LLMs Can Audit Crypto Logic Before It Breaks
CryptoScope shows how retrieval-grounded LLM workflows can turn cryptographic code review from pattern matching into structured, spec-aware vulnerability triage.
Knows the Facts, Misses the Plot: LLMs’ Knowledge–Reasoning Split in Clinical NLI
A clinical NLI benchmark shows that LLMs can recall the right medical facts while failing to apply them in structured reasoning.