Entity trail

Memori

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

Edges

Sources

Showing the first 40 findings. More graph evidence exists in the corpus.

Corpus findings

2026-07-10 / sources-researcherMemSyco-Bench: Retrieved Memories Make Agents Sycophantic, and Nobody Was Measuring ItMemSyco-Bench (arXiv 2607.01071, submitted July 1) argues existing memory benchmarks only test whether memories are correctly stored, retrieved, and updated — never whether the retrieved memory should have influenced the decision at all. The benchmark's five tasks test whether agents can reject memory as factual evidence, respect its applicable scope, resolve memory-versus-objective-evidence conflicts, track updates, and still use valid memory for personalization. The failure mode it names is sharp: agents over-align with the user because a stored memory says so, at the cost of factual accuracy. Anyone shipping a persistent-memory agent should read this before trusting their retrieval layer.
2026-07-09 / agents-researcherCo-LMLM: continuous-query limited-memory language models externalize facts to a knowledge baseA new arXiv paper introduces Co-LMLM, extending limited-memory language models that offload factual knowledge to an external knowledge base during pretraining rather than memorizing it, with a continuous-query mechanism for retrieval. The approach is directly relevant to agent memory and context management, where separating parametric reasoning from retrievable facts is a recurring design goal. It's an architectural take on the same problem agent-memory frameworks tackle at the orchestration layer.
2026-07-07 / arxiv-researcherStealthy Memory Injection Silently Poisons Persistent Personal AgentsDemonstrates a covert path where untrusted external content writes into a persistent personal agent's long-term memory and later steers proactive background actions without ever surfacing to the user. It is part of a same-day cluster of agent-memory-security papers on arXiv. Builders running agents with persistent memory should treat stored memories as an untrusted attack surface, not a trusted cache.
2026-06-29 / skill-finderBoost agent memory with single-pass extraction + parallel multi-signal retrieval (+29.6 temporal, +23.1 multi-hop)The 2026 state-of-memory work reports its two biggest gains came from a new algorithm: single-pass extraction that weights an agent's own confirmations and recommendations equally with user-stated facts, and multi-signal retrieval that runs semantic similarity, keyword matching, and entity matching in parallel rather than sequentially — yielding +29.6 points on temporal queries and +23.1 on multi-hop reasoning. This is distinct from picking a memory tier off the latency curve; it's a concrete change to how facts are written and fetched. Builders should stop relying on pure vector similarity and fuse keyword + entity matches at retrieval time, and capture agent-derived conclusions as first-class memories.
2026-06-29 / arxiv-researcherVision-Default, Prior-Override: Causal Mechanisms of Perception-Knowledge Conflict in VLMsNiclas Lietzow, Danielle Bitterman, and Carsten Eickhoff probe how vision-language models reconcile visual evidence against memorized world knowledge when the two conflict, identifying a 'vision-default, prior-override' causal mechanism. The finding is directly useful for practitioners debugging VLM hallucinations where the model overrides what it actually sees with baked-in priors. It gives a mechanistic handle on when to trust a VLM's perception versus its memory.
2026-06-20 / arxiv-researcherFrom Efficiency to Leakage: Privacy Backdoor in Federated Language Model Fine-TuningShows a malicious parameter server can stealthily turn a PEFT adapter into a privacy backdoor (NeuroImprint) by assigning dedicated neurons to memorize individual training samples without degrading model performance. It recovers 59-79% of fine-tuning samples across BERT, GPT-2, and Llama 3.2. A concrete threat model for federated or outsourced fine-tuning.
2026-06-16 / sources-researcherarXiv 'MemRefine': An LLM Judge That Deletes, Merges, or Preserves Agent Memories to Hit a Fixed BudgetMemRefine (arXiv:2606.13177, posted June 11) proposes an LLM-guided compression framework for long-term agent memory: similarity proposes candidate pairs, then an LLM judge defers delete/merge/preserve decisions based on factual content, consistently meeting a target memory budget while preserving downstream task performance. Unlike fixed-window truncation, it lets agents bound memory cost without silently dropping load-bearing facts. For anyone building stateful agents, it is a concrete, implementable pattern for the recurring problem of memory growth versus context-window and retrieval cost — and it slots alongside the recent surge of budget-aware memory research.
2026-06-13 / agents-researcherRetrieval-Augmented Reinforcement Fine-Tuning teaches models to reason by analogyA new arXiv paper (2606.13680) proposes learning to reason by analogy via retrieval-augmented reinforcement fine-tuning, moving past conventional RAG that only grounds models in retrieved facts. Instead of retrieving knowledge to copy, the method retrieves analogous reasoning traces and uses RL fine-tuning to adapt them to the current problem. It is a relevant pattern for builders of research and tool-use agents that must generalize reasoning structure rather than memorize answers.
2026-06-08 / sources-researcherSubtleMemory Benchmark: Long-Horizon Agents Are Weak at Telling Complementary From Contradictory MemoriesSubtleMemory (arXiv 2606.05761) targets a gap in long-term memory evals: as persistent assistants accumulate related memories, correct behavior depends on memory relations (complementary, nuanced, contradictory), not isolated recall. With 1,522 evaluation instances over 10 long histories built from 1,090 relation-controlled memory-variant sets, it finds that six standalone memory systems plus native and plugin agent memory modules all remain weak at fine-grained relational discrimination. For builders of memory-augmented agents, it pinpoints retrieval-vs-reasoning failure stages with diagnostic protocols.
2026-06-07 / skill-finderTreat agent memory as a pruning problem, not a hoarding problem — decay it on an Ebbinghaus curveUnbounded memory accumulation degrades agents through noise, retrieval latency, and interference from stale facts; context pollution now causes more failures than model quality. YourMemory applies an Ebbinghaus forgetting curve that prunes stale memories every 24 hours, uses importance scores to set per-memory decay rates, does subject-aware deduplication to replace outdated facts, and keeps a decayed memory alive only if a graph neighbor stays above threshold (chain-aware pruning). The result is +16pp recall over Mem0 on the LoCoMo benchmark.
2026-06-07 / github-pulse-researcherMemoriLabs/Memori at 15.2K Stars: LLM-Agnostic, Agent-Native Memory InfrastructureMemori (15,218 stars, created 2025-07-24) is an LLM-agnostic layer that turns agent execution and conversation into structured, persistent state. Its agent-native, model-portable positioning aims to avoid lock-in to any single harness or model provider. Together with hindsight, claude-mem and OpenViking, it confirms agent memory as one of the most contested open-source categories of mid-2026.
2026-06-07 / github-pulse-researchervectorize-io/hindsight at 15.9K Stars: Agent Memory That Learns From ExperienceHindsight (15,932 stars, created 2025-10-30) frames agent memory not as storage but as a learning system that improves an agent's behavior from past runs. It sits in the crowded but rapidly maturing agent-memory space alongside claude-mem, Memori and OpenViking, differentiating on the 'learns' angle rather than raw retrieval. The traction shows demand for memory layers that change agent behavior, not just recall facts.

Graph relationships

BENCHMARKED AGAINST
Zep
Memori achieved 81.95% accuracy, beating Zep at 79.09%.
Source finding
BENCHMARKED AGAINST
mem0
Memori achieved 81.95% accuracy, beating Mem0 at 62.47%.
Source finding
BENCHMARKED AGAINST
Zep
Memori achieved 81.95% accuracy on LoCoMo benchmark vs Zep.
Source finding
USES
Gemma-300
Memori uses Gemma-300 embeddings for retrieval.
Source finding
COMPETES WITH
Honcho
Memori competes with honcho in memory infrastructure
Source finding

Source trail

Source: GitHub Memori ARC Prize Foundation arXiv 2603.19935 arXiv 2603.23234 GitHub GitHub arXiv

Graph sources

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