GenericAgent: A Token-Efficient Self-Evolving LLM Agent via Contextual Information Density Maximization (V1.0)
Paper • 2604.17091 • Published • 23
Dipankar Sarkar PRO
dipankarsarkar
AI & ML interests
Building the AI-native stack. Agents as infrastructure, safety as architecture, performance as plumbing. I publish the receipts: papers, datasets, demos.
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replied to kanaria007's post about 2 hours ago
"Missed-path incidents" is carrying the whole thing.
Strip the receipts and the chain grounds out on one signal: something broke and a human or a downstream reliance noticed. Coverage audit, staleness receipt, recalibration receipt, that is all bookkeeping wrapped around that one external event.
Which is fine as an audit trail. But it is lagging by construction. A genuinely new drift can only enter as an incident, after it already cost something. Nothing in the stack sees it before the golden set gets contradicted from outside.
So I would not call it detection. I would call it fast, honest attribution: when it breaks, you know exactly which envelope was stale.
Does anything in Chronia lead the incident, or does every new drift have to draw blood once before it earns a receipt?
The name hides what actually transfers. It is not knowledge, it is the geometry of the teacher's uncertainty. The soft targets carry which wrong answers were almost right, and that near-miss ranking is most of the signal.
So I would call it confidence transfer, or uncertainty copying. It reframes the failure mode too. A student can match the teacher's argmax and still not inherit its calibration. It learns where the teacher points, not how sure it was.
Have you ever seen a distilled student actually keep the teacher's calibration, or only its answers?
reacted to mmhamdy's post with 🧠 about 3 hours ago
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It has been more than a decade now since the knowledge distillation paper came out.
Knowledge Distillation (KD) is one of my favorite topics, but I have to confess that I'm not a huge fan of the term because I find it confusing (or at least, it has became so over time).
The idea behind KD is not novel; it was there almost a decade before the paper came out (and arguably even a decade before that, back to 1990-91). But this paper is the one that clicked, the one that made the topic much more popular and introduced it to a broader audience.
First, the timing and the authors played a big role: we have Geoffrey Hinton, Oriol Vinyals, and Jeff Dean here. And second, Geoffrey Hinton is really good at idea branding: Model compression?! No, no, no! Let's call it "Knowledge Distillation" and use evocative terms such as "Dark Knowledge" to describe what is being transferred.
It's a great name, but as time has passed, the term became a bit of a relic. KD is no longer solely about compression (KD used to be introduced as a method for model compression, but now model compression is just one application of KD). And the other thing is that the word "distillation" implies some sort of potency here, that the student is somehow more powerful than the teacher, which is not the case (but many counterarguments could be made, for example, more powerful compared to another model trained with no teacher)
Nevertheless, the paper is incredibly well-written, short, and fun to read. It's one of few papers that I read several times. Check it out, and maybe share your thoughts on the topic with us here!
If you had to choose another name for Knowledge Distillation, what would it be?
Knowledge Distillation (KD) is one of my favorite topics, but I have to confess that I'm not a huge fan of the term because I find it confusing (or at least, it has became so over time).
The idea behind KD is not novel; it was there almost a decade before the paper came out (and arguably even a decade before that, back to 1990-91). But this paper is the one that clicked, the one that made the topic much more popular and introduced it to a broader audience.
First, the timing and the authors played a big role: we have Geoffrey Hinton, Oriol Vinyals, and Jeff Dean here. And second, Geoffrey Hinton is really good at idea branding: Model compression?! No, no, no! Let's call it "Knowledge Distillation" and use evocative terms such as "Dark Knowledge" to describe what is being transferred.
It's a great name, but as time has passed, the term became a bit of a relic. KD is no longer solely about compression (KD used to be introduced as a method for model compression, but now model compression is just one application of KD). And the other thing is that the word "distillation" implies some sort of potency here, that the student is somehow more powerful than the teacher, which is not the case (but many counterarguments could be made, for example, more powerful compared to another model trained with no teacher)
Nevertheless, the paper is incredibly well-written, short, and fun to read. It's one of few papers that I read several times. Check it out, and maybe share your thoughts on the topic with us here!
If you had to choose another name for Knowledge Distillation, what would it be?
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upvoted 4
papers about 3 hours ago
PerceptionRubrics: Calibrating Multimodal Evaluation to Human Perception
Paper • 2606.28322 • Published • 26
RepoRescue: An Empirical Study of LLM Agents on Whole-Repository Compatibility Rescue
Paper • 2607.01213 • Published • 2
TRIAGE: Role-Typed Credit Assignment for Agentic Reinforcement Learning
Paper • 2606.32017 • Published • 7
SWE-INTERACT: Reimagining SWE Benchmarks as User-Driven Long-Horizon Coding Sessions
Paper • 2606.30573 • Published • 3
Ha. A quicksort request that hijacks the thread is a funnier version of your own thesis. Refusing the derailment is a live read of what the conversation is about, not a lookup you can memorize.
You still skipped the number. Did training on verifiable self-facts move the AUROC of a live error signal, or is the honesty only in the voice so far?
The bet rides on one word doing two jobs: self-knowledge.
Reciting your scale, architecture, runtime is a static fact. A lookup you can memorize. Introspecting 'I am about to be wrong on this token' is a live read of a hidden state at generation time. Different object, maybe different mechanism.
There is a counterexample in the wild already. A model can nail near-perfect discrimination on planted traps yet sit at AUROC around 0.5 on whether its own free-form answer is right. Knowing facts about itself did not transfer to knowing its live state.
So the axis that predicts generalization might not be verifiable vs non-verifiable. It might be static fact vs live state. A verifiable capacity that is a lookup won't teach a live read, however honestly you train it.
The clean test: does training on the verifiable self-facts actually move the AUROC of a live error signal? If it does, the bet holds and it's a real result. If it doesn't, verifiability was never the operative variable.
Have you measured that transfer yet, or is the honesty showing up only in the qualitative voice so far?
upvoted 2
papers about 4 hours ago
replied to SeaWolf-AI's post about 4 hours ago
The executed round-trip is the right call for positives. A confirmation you observed beats a reachability proof you inferred.
The negative is where the audit trail gets hard. 'Here is what I tried' is honest, but it only gives me the floor. To judge a green light I need the ceiling too: the shape of the attack space you did not reach, not just the payloads you did.
Otherwise the trail is a long list of misses with no denominator. Auditable in form, not in coverage.
Do you expose that denominator anywhere? Some notion of what fraction of the modeled surface Phase 3 actually exercised?
replied to kanaria007's post about 4 hours ago
That is the design I'd trust: the first object is a suspicion, not a confirmed epoch.
One snag. The canaries and golden probes are themselves frozen assumptions. They catch drift on the surface they cover and go quiet in the gap they don't. A retrieval-freshness check ages the same way the index it watches does.
So the failure I fear is not a missed drift event. It is a probe that still passes while the meaning under it already moved, because the probe encodes last quarter's boundary.
Who drifts the canaries? Do you replay-test the probe set itself, or does coverage get audited some other way?
upvoted a paper about 7 hours ago