My last 2 papers with time stamped logs in continuity and emerging personality in LLM based entities & how memory reduces tokens consumption and developers time
Summary
The author presents two papers examining time-stamped logs for continuity and emerging personality in LLM-based entities, and how memory reduces token consumption and developer time.
Similar Articles
A paper on “memory provenance laundering” in LLM agents
A paper explores 'memory provenance laundering' in LLM agents, where long-term memory can turn untrusted observations into seemingly trusted context, and proposes preserving provenance through memory consolidation.
Practical criticism of: Long-running-sessions, Life-companions, "LLM-wiki", Memory. Solutions: Immutable reflections, Issue-bound task-bound ephemeral-session chains, Prompt-templates, Independent criticism, Prototypes
The article presents a practical critique of long-running LLM sessions, life-companion agents, and persistent memory systems, raising issues of privacy, cost, intent-loss, and maintainance. It proposes alternative solutions like issue-bound ephemeral session chains and prompt templates.
Never waste a token (15 minute read)
A technical blog post explaining how to avoid wasting LLM tokens by placing a durable buffer between the agent and the provider, enabling recovery from process crashes without re-fetching already-generated tokens.
@dylan_works_: Wrote up something fun I’ve been poking at: when LLM agents repeatedly rewrite their own experiences into textual “less…
This research blog post demonstrates that repeatedly rewriting LLM agent experiences into textual 'lessons' often degrades performance rather than improving it. The author finds that episodic memory retention performs better than abstract consolidation across various benchmarks like ARC-AGI and ALFWorld.
Shared Selective Persistent Memory for Agentic LLM Systems
This paper introduces shared selective persistent memory for agentic LLM systems, which retains reusable context like task specs and data schemas while discarding session-specific reasoning traces, achieving 96% task completion and significant token cost reductions in enterprise scenarios.