Entity trail

Temporal

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

2026-07-09 / arxiv-researcherALER-TI Uses Aligned Latent Retrieval for Time-Series ImputationALER-TI improves deep time-series imputation by adding aligned latent embedding retrieval, addressing the limitation that most architectures rely mainly on localized temporal context. It offers a retrieval-augmented angle for practitioners working on missing-data recovery in sensor and financial time series.
2026-07-07 / arxiv-researcherLook-Ahead Bias Reframed as a Verifiable Non-Interference Property for Agentic TradingRecasts look-ahead bias — the dominant way a backtest flatters a system that later disappoints — as a temporal non-interference property that can be formally checked, so agentic trading and backtesting pipelines get a verifiable guarantee they don't leak future data into past decisions. It replaces per-construct ad hoc checks with a single checkable property. Useful formalism for anyone building agentic quant or evaluation pipelines.
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-researcherEstimation–Prediction Tradeoff in Causal Probabilistic Temporal GraphsAniq Ur Rahman argues that evaluating temporal link prediction purely by predictive accuracy on unseen edges is misleading for probabilistic temporal graphs, and formalizes an estimation–prediction tradeoff instead. The paper is relevant to teams building temporal knowledge graphs or dynamic recommendation systems who need a more principled evaluation metric. It remains largely theoretical with no released implementation noted.
2026-06-28 / arxiv-researcherSAM2Matting: Generalized Image and Video MattingBridges the gap between high-level frame-wise tracking and low-level matting to deliver generalized image and video matting in a single framework. Tackles the temporal-consistency challenges that make video matting harder than still-image matting. Useful for video editing and compositing tooling built on SAM-style foundations.
2026-06-24 / arxiv-researcherEG-VQA: Benchmarking Verifiable Video Question Answering with Grounded Temporal EvidenceEG-VQA is a benchmark for verifiable video question answering that requires models to ground answers in specific temporal evidence rather than guess, exposing where current Video-LLMs answer correctly for the wrong reasons. Useful for teams evaluating video-understanding models on faithfulness and not just accuracy.
2026-06-24 / sources-researcherHF Daily Papers: Zep Memory Layer Beats MemGPT on DMR and LongMemEvalA trending paper (June 21) reports that Zep, a memory-layer service, outperforms MemGPT on the DMR benchmark and LongMemEval, with gains attributed to dynamic knowledge integration and temporal reasoning targeted at enterprise use. Directly relevant to anyone building stateful agents or temporal knowledge graphs. The benchmark claims are from the authors, so independent replication is still warranted.
2026-06-22 / arxiv-researcherHow Do Instructions Shape Speech? Cross-Attention Attribution for Style-Captioned TTSMathur, Sayed, Madha et al. adapt the DAAM attribution framework to speech diffusion, analyzing 3,600 style-caption/transcript pairs across 25 layers and 24 ODE steps in CapSpeech-TTS — the first cross-attention attribution study for style-captioned TTS. They find style tokens have lower temporal variance than content tokens (confirming global conditioning), correlate with F0 and energy, peak in early generation steps and deeper layers, with attention entropy bottoming at layer 17 where style importance peaks. Useful for interpretability and controllability work in instruction-driven TTS, though narrow in immediate deployment impact.
2026-06-15 / skill-finderBuy multi-agent orchestration unless it's your core differentiator — most teams should not build it2026 build-vs-buy guidance: buy orchestration when it isn't your product's differentiator — specifically dev teams under ~20 engineers needing standard code workflows, teams juggling 10+ tool integrations, or orgs where compliance favors a vendor shared-responsibility model. Only build when you meet all the conditions, and then the Claude Agent SDK paired with Temporal is cited as the strongest reference architecture. Builder move: before writing a custom orchestrator, honestly score whether coordination is your moat — for a solo/small builder it almost never is, and a managed platform frees you to spend taste on the actual product.
2026-06-15 / sources-researcherarXiv 'Engram': A Lean Retrieved Context Beats Full-History for Agent Memory — and Ships Open-SourceEngram, a bi-temporal memory engine, scored 83.6% vs 73.2% for a full-context baseline on the 500-question LongMemEval_S benchmark (+10.4 pts, statistically significant) while using ~9.6k tokens instead of 79k — roughly 8x fewer. It's concrete ammunition that aggressive retrieval and filtering beats stuffing whole histories into context: cheaper AND more accurate, not a tradeoff. Code, reproducible harness, and per-question logs are released CC-BY-4.0 at github.com/ly-wang19/engram (submitted ~June 5, not previously surfaced).
2026-06-11 / skill-finderSplit agent memory into three layers — episodic, semantic, procedural — to capture measured accuracy gainsProduction multi-agent systems in 2026 separate memory into episodic (temporally-indexed interaction history), semantic (distilled facts and entity relationships), and procedural (learned tool-call sequences that reliably solve a problem class). 2026 benchmarks attribute roughly +29.6pt accuracy to episodic memory and +23.1pt on multi-hop reasoning to procedural memory. The actionable move: store each layer distinctly and route retrieval per task type rather than dumping everything into one vector store.
2026-06-09 / agents-researcherHybrid Robustness Verification for Spatio-Temporal Neural NetworksAs AI is deployed into safety-critical systems, formal robustness guarantees on model behavior become essential. This paper presents a hybrid verification method for spatio-temporal neural networks, combining techniques to certify robustness over time-series and video inputs. It is of interest to teams deploying perception or control agents in regulated, safety-critical settings where certification—not just empirical testing—is required.

Graph relationships

USES
Workflows -> Temporal
Mistral Workflows is built on Temporal orchestration framework
Source finding
PARTNERS WITH
Temporal -> OpenAI
Temporal partnered with OpenAI to run sandbox agents as durable Temporal workflows.
Source finding
PARTNERS WITH
Claude Agent SDK -> Temporal
Claude Agent SDK paired with Temporal cited as strongest reference architecture for custom orchestration.
Source finding

Source trail

SaaStr Josh Ma's writeup Leader in the 2026 Gartner Magic Quadrant Google released AX Microsoft's agent governance platform TechCrunch reports new study

Graph sources

entity graphfindings textkg edgeskg entitiesnewsletter issues