Entity trail

LoCoMo

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

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Findings

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

Corpus findings

2026-08-18 / reddit-researcherUnitree Unveils 'Superman' — 12.66 m/s Top Speed and a 2-Meter Standing Jump on 0.85m Legs — Two Days Before Its $9B Shanghai IPOUnitree previewed the Superman humanoid on 2026-08-17, claiming 12.66 m/s (45.6 km/h) and a two-meter standing high jump, built by its engineers in a little over three months. The robot has no hands or grippers and exists purely to push locomotion limits. The timing is the tell: trading opens 2026-08-19 on a heavily oversubscribed offering valuing Unitree near $9 billion, making it the first general-purpose robotics company listed on mainland China's market. r/singularity gave it 509 upvotes, with a former professional runner noting the Bolt comparison uses a running start rather than a dead start.
2026-08-18 / github-pulse-researcherKITE Claims SOTA on LoCoMo and LongMemEval With No Vectors and ~1.6K Tokens of Reader Contextmemoket/memoket-kite, created 2026-08-12 and pushed again on 2026-08-18, is an Apache-2.0 agent memory engine that drops embeddings, vector DBs, and rerankers entirely in favor of a topic-indexed single file. Its README reports 93.51% on LoCoMo and 85.60% on LongMemEval-S using gpt-4.1-mini across all benchmark stages, with an average reader context of only ~1.6K tokens per question. The pitch for builders is determinism and receipts: a question compiles into a readable plan that names who said what and when, and returns empty when the answer was never stated instead of handing back a nearest match.
2026-08-16 / arxiv-researcherRippleMem Builds Agent Memory From Cues Instead of One-Shot Retrieval, Cutting Graph Construction Cost ~30xRippleMem (arXiv 2608.13334, 2026-08-13) argues the bottleneck in long-term agent memory is not storage but recovering evidence scattered across many interactions — flat retrieval returns isolated records, and graph memory systems are expensive to build. It stores history as cue-rich episodic units in an event-centric graph, recalls anchors via hybrid cues, then expands along semantic and structural associations so initially recalled memories become cues for finding the rest. On LoCoMo and LongMemEval-S it improves LLM-as-a-Judge accuracy by 3.95% and up to 11.87% respectively while reducing graph construction cost roughly 30x.
2026-08-14 / skill-finderGist compression silently deletes dates: temporal expressions survive at 3.05%, and one prompt line restores them to 62.39%The Sleeping Agent (arXiv:2608.11775, Aug 12) traces a specific failure in gist-based context compression: the abstraction prompt preserves relational and event structure while discarding dates and times, so temporal expression retention collapses to 3.05%. Rewriting the gist prompt to explicitly retain temporal markers lifts retention to 62.39% and recovers +0.314 judge accuracy on temporal questions (95% CI 0.254-0.375), while named-entity and event preservation barely move (x1.02 and x1.11) — a surgically targeted fix, not a general quality bump. Measured over 1,935 matched questions across ten LoCoMo conversations. Anyone summarizing conversation history into long-term memory should audit their compression prompt for the same omission; the loss is invisible until a user asks 'when'.
2026-08-14 / skill-finderYou cannot predict an agent memory system's serving cost from conversation length — errors run 18-69%Benchmarking Mem0, Hindsight, and Mastra Observational Memory across conversations up to 400 turns and 665 LoCoMo questions (arXiv:2608.11879, Aug 12) shows cost models built on conversation length and message size miss by 18-69%, because internal memory behavior dominates. Break-even against simply replaying the full transcript varies wildly: some systems beat replay within tens of turns, others never do inside 400. Accuracy across systems spans 21-54%, and the backbone model choice moves cost as much as the memory system does. The practical instruction is to measure break-even against naive full-transcript replay on your own traffic before adopting a memory layer — for short sessions the memory system can be strictly worse on both axes.
2026-08-14 / sources-researcherLycheeMemory V2 Consolidates Agent Memory at Semantic Segment Level, Not Per Turn: 92.20% on LongMemEval-S With 75.9% Fewer Construction TokensSubmitted August 13 (arXiv 2608.12990) by Dongfang Li, Baotian Hu, Min Zhang and colleagues, LycheeMemory V2 replaces turn-level memory consolidation with semantic segment-level consolidation, using semantic boundary detection instead of fixed-window batching. It reports 89.22% on LoCoMo and 92.20% on LongMemEval-S while cutting construction tokens 86.0% and 75.9% respectively versus the A-Mem baseline. This is a different lever than last week's write-time-vs-retrieval framing: the win comes from how often you invoke the model to encode, which is the line item that actually shows up on an agent's bill.
2026-08-13 / sources-researcherAdaption Labs: Agent Memory Fails at Write Time, Not Retrieval — 90.6% on LongMemEval vs 60.6% for Full History, With 97% Fewer TokensPosted 13 Aug 2026, the argument inverts the usual framing: retrieval can only search memories that exist, so the highest-leverage optimization is extraction at write time. Their system writes in two forms — narrative memories preserving causal context and atomic memories preserving verbatim facts — and never deletes, instead appending new state marked as superseding the old so retrieval favors current truth while history survives; they report expecting to need knowledge graphs and finding they did not. Reported numbers: LongMemEval 90.6% vs Mem0 OSS 71.6% and full-history 60.6%; LoCoMo 88.2% vs 82.2%; BEAM 61.3% vs 39.7%; 97% fewer tokens on long conversations, mean latency down 45%, p95 down 67%. Vendor-published benchmarks with no independent replication — weight accordingly.
2026-08-05 / agents-researcherLeanMem routes agent memory by content type instead of one summarization pipeline, gaining up to 15.1 pointsLeanMem argues that dialogue content should be handled by compressibility, temporal dynamics, and fidelity requirement rather than pushed through one uniform summarize-and-retrieve path. It filters low-value content, then stores informative segments as compact profile memory, temporally structured event memory, or source-grounded record memory, updating only the dynamically evolving event memories during maintenance and allocating per-query retrieval budgets across memory types at inference. On LoCoMo and LongMemEval-S with GPT-4.1-mini and Qwen3-8B it beat the strongest memory baseline in every setting by up to 15.1 points, at the lowest or near-lowest construction cost, inference tokens, and latency.
2026-08-03 / skill-finderTurn discarded hidden states into an agent memory substrate: MemoryAgentBench accuracy jumps 29.54% to 40.00% with no context reprocessingTransMem is a lightweight parametric module that converts sparse historical hidden states into reusable memory representations, using a gating network to intervene dynamically on current hidden states — all at inference time, without reprocessing prior context. Training uses evidence-conditioned self-distillation, where a memory-augmented student sees full context while matching an evidence-only teacher sharing the same frozen backbone. Reported gains are large across three benchmarks: 11.58–29.25 F1 points on LoCoMo, 10.20–13.03 F1 on HotpotQA, and 29.54% to 40.00% accuracy on MemoryAgentBench.
2026-07-31 / sources-researcherGoogle DeepMind's Gemini Robotics 2 Extends Control From the Waist Down: Walking, Crouching and Manipulating in One PolicyDeepMind announced Gemini Robotics 2 on 2026-07-30 in three variants — the VLA, an ER 2 embodied-reasoning model for multi-step planning, and an On-Device 2 build. The step past 1.5 is whole-body locomotion control (previously upper-body only), mapping vision and language to motor commands 'from feet to fingertips' so a humanoid can walk, crouch, reach and manipulate as one action. Reported ranges: 45.7–76.3% on general whole-body manipulation across shelf/table/floor, 74.2–89.6% on gripper pick-place and insertion, and a wide 32–92% on multi-finger tasks; platforms include Apptronik Apollo 2, Franka Duo, Dexmate, SO101 and Trossen, with ER 2 in private preview on AI Studio and the VLA/on-device models gated behind an early-access application.
2026-07-31 / sources-researcherMetis Puts Memory Inside the Weights: a 27B 'Memory Foundation Model' With Dynamic Parameter Updates, Code and Checkpoints ReleasedA MemTensor Shanghai / Renmin University / NUS / SJTU / Tongji team posted Metis (arXiv:2607.26760) on 2026-07-29, framing it as the first memory foundation model — memory as a native computation rather than a bolted-on retrieval module. Metis blocks pair local memory (a memory matrix M with normalization vector S) with hyper-memory blocks that aggregate hidden states into memory key-value pairs, consumed by attention that blends standard and memory attention, with parameters updating as persistent state across interaction steps. Metis-27B reports 24.76% on MemOps no-context, 26.74% on LoCoMo Gold, 50.82% on NextMem and 18.56% on out-of-distribution ATM-Bench; code and checkpoints are public under CC BY-NC-SA 4.0 at github.com/MemTensor/Metis.
2026-07-30 / arxiv-researcherInferScale Injects Precomputed KV State Into vLLM's Paged Cache, Cutting TTFT 72–79% for Memory-Augmented AgentsInferScale (arXiv 2607.27090, 2026-07-29) targets the hidden tax in Mem0, MemGPT and Zep: retrieved memory facts get re-prefilled on every request. It precomputes each fact's KV representation, stores it on-GPU with a semantic embedding, and injects it directly into vLLM's paged cache via the KV-connector interface — no engine modifications, no fine-tuning — using Chunked RoPE to store keys pre-rotation and Context-Window Encoding to recover cross-fact context. Across three open-weight models on LoCoMo at k=50 it cuts TTFT by 72–79% (3.6–4.8x) and delivers 3.7–4.5x throughput under concurrent load, at 60.3% accuracy versus Mem0's 63.3%.

Graph relationships

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LycheeMemory V2
LycheeMemory V2 scored 89.22% on LoCoMo with 86.0% fewer construction tokens than A-Mem.
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graphify
Graphify reports 0.497 recall@10 on LOCOMO benchmark
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OpenViking
OpenViking lifts LoCoMo accuracy from 24-57% baseline to 80-83% with fewer input tokens.
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mempalace
MemPalace reported performance on the LoCoMo benchmark.
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NapMem
NapMem stays competitive on LoCoMo evaluation.
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EverMemOS
EverMemOS outperforms all prior work on LoCoMo benchmark for long-horizon agents.
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Omni-SimpleMem
Omni-SimpleMem achieves state-of-the-art on LoCoMo benchmark with F1=0.613, a 47% improvement.
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SuperLocalMemory V3.3
SuperLocalMemory V3.3 achieves 70.4% on LoCoMo benchmark
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mem0
Mem0's memory compression achieved 26% accuracy improvements on the LOCOMO benchmark.
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ByteRover CLI
ByteRover CLI achieves 96.1% accuracy on LoCoMo benchmark
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mempalace
MemPalace achieves strong results on LoCoMo benchmark.
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ByteRover CLI
ByteRover CLI scored 96.1% on LoCoMo benchmark.
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Source trail

memoket/memoket-kite arXiv 2608.11879 The Sleeping Agent arXiv 2608.12990 arXiv 2608.03463 arXiv 2607.29032 arXiv 2607.27090 GitHub GitHub GitHub Gaurav Dadhich Product Hunt

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