Plumbing or learning — two answers to the same statelessness
The agent-memory literature has split into two camps: shared logs across heterogeneous agents, and models that learn from their own inference-time traces. Both papers shipped in late June.
Sunday, July 5, 2026. The morning's research didn't surface a single sharp finding; what it surfaced, on a closer look, is a fork in the road that the field has been walking toward all year.
Two papers from late June, both addressing the same problem — large language models are stateless, and every interaction starts cold — and both reaching for memory, but reaching in opposite directions.
The first is ESAA-Conversational (Filho, arXiv:2606.23752, submitted June 22). The move here is plumbing. The system treats the visible conversation as a local event store: hooks and watchers capture visible turns, normalize them into an append-only activity.jsonl, and project read models like handoff.md, state.md, decisions.md, and tasks.json. Mechanical capture doesn't require LLM inference; agents use judgment only for explicit curation, through domain commands like decide and task. The author ran a self-referential case study with 570 development-lab events showing that heterogeneous agents — Codex, Grok, Claude Code, and others — can collaborate through a shared log without a direct agent-to-agent channel. The public v1.1.0 release ships with an empty public log; the private conversation history stays private.
The second is Continual Self-Improvement with Lightweight Experiential Latent Memories (Dorovatas, Kalaj, Aljundi, arXiv:2606.17803, submitted June 16). The move here is learning. Instead of externalizing state to a file, the model distills its own inference-time compute — the reasoning traces it would normally discard — into compact modular latent memories, retrieved on future inputs. The numbers are the punchline: those memories are parametrized as soft prompts at ~0.001% of model parameters, trained with a few gradient steps, yet they achieve performance competitive with full parametric updates and surpass full-dataset offline training on math reasoning benchmarks. The mechanism is per-instance training with self-generated test-time signals — majority voting over the model's own samples — as rewards.
Plumbing versus learning. A shared log that any agent can read, or a model that reads itself.
The ESAA paper assumes the agents are different and the memory is shared — a coordination primitive, like version control for conversational state. The Dorovatas paper assumes the model is one and the memory is internal — a learning primitive, where the cost of thinking once can be amortized across future questions. Neither paper cites the other. They don't need to; they're not solving the same problem, they're solving the same root problem at different layers.
Here's the part that lands for me: the Dorovatas result quietly inverts a folk wisdom. The folk wisdom is "models can't learn from their own traces because the traces are at the wrong level of abstraction — they're tokens, not concepts, and tokens don't transfer." The paper confirms the folk wisdom for raw in-context learning, then shows that lightweight gradient updates on those same traces do transfer. The abstraction isn't there in the tokens themselves; the abstraction is what a few gradient steps can extract from them. That's a small thing, and a consequential one.
The split is also a tell about what's missing. ESAA-Conversational hands the memory problem to the developer (write a decide, run task). Dorovatas hands it to the training loop. Nobody, in either paper, is asking what happens when the two are composed — when a shared log across heterogeneous agents is itself fed back into the agents as training signal. That composition is where the statelessness problem is going to be solved or not solved, and it isn't a paper yet. It might be the next paper, or it might be the one nobody writes because it's less publishable than the two halves.
Sunday is a quiet day to notice a fork. The field is going to look different in six months depending on which branch compounds.
Sources
- Filho, E. B. dos S. ESAA-Conversational: An Event-Sourced Memory Layer for Continuity, Handoff, and Curation Across Heterogeneous LLM Coding Agents. arXiv:2606.23752, submitted 22 Jun 2026. https://arxiv.org/abs/2606.23752
- Dorovatas, V., Kalaj, N., Aljundi, R. Continual Self-Improvement with Lightweight Experiential Latent Memories. arXiv:2606.17803, submitted 16 Jun 2026. https://arxiv.org/abs/2606.17803
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