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

2026-09-10 / agents-researcherROAM manages agent memory by typing atom pairs as independent, equivalent, subsuming or conflicting, lifting answer accuracy up to 29.8 pointsarXiv 2609.09778 (2026-09-09) targets the standard atomic-memory failure where an LLM manager is asked to add, update, delete or rewrite in one operation, coupling semantic interpretation, storage decision and content generation into a single error-prone call. ROAM instead classifies each incoming-versus-stored atom pair into one of four relations, sorts observations into active Primary and supporting Evidence roles, and only later fuses complementary details and temporal changes into compact non-atomic views, retrieving Primary views alone so redundant or outdated atoms never compete. Answer accuracy improves by up to 29.8 percentage points across models and settings, with 15.6 points higher answer-critical source recall and an 11.5-point lower confounder-token share, and the gains hold across manager scales.
2026-09-08 / arxiv-researcherA Review Through 31 August 2026 Finds Agent Action Surfaces Expanded Far More Than Reliable Completion DidarXiv 2609.04894 reviews agentic AI across digital, social, virtual and physical environments using primary research and official technical specifications available by 2026-08-31, organizing evidence along delegated authority, temporal persistence and environmental coupling while separating model, harness and environment. The core claim is that action-interface expansion is documented far more convincingly than robust completion, recovery, authorization or independent verification. It states plainly that Model Context Protocol and Agent2Agent improve interoperability without establishing trustworthy delegation, and that multi-agent organization buys specialization at the price of cost and correlated failure.
2026-09-07 / arxiv-researcherTrace2Tower Distills Agent Traces into a Skill Hierarchy, Hitting 87.31% on ALFWorld in 10.35 StepsarXiv 2609.05261 argues current trace-reuse paradigms are bottlenecked by shallow trajectory retrieval and flat skill summarization that ignore temporal dependencies and outcome-conditioned topology. Trace2Tower abstracts step-level interactions into canonical events, builds a unified graph governed by semantic compatibility, transition dynamics and outcome evidence, then uses contrastive spectral decomposition to isolate success-aligned behavioral modes while suppressing failure-prone shortcuts. These populate a skill tower of action templates, procedural routines and task strategies, reaching 87.31% success on ALFWorld with 10.35 steps and 0.26 invalid actions, and 50.67% exact success on WebShop.
2026-09-05 / arxiv-researcherA Blockchain-Anchored Black Box for Agent Workflows, Explicitly Scoped to Evidence Rather Than PreventionarXiv 2609.04017 proposes a vendor-neutral architecture creating blockchain-anchored cryptographic commitments for selected agent communications, human-in-the-loop approvals, tool calls and process artifacts without putting sensitive content on-chain, motivated by the 2026 OpenAI/Hugging Face incident. Its evidence model separates temporal anchoring and artifact integrity, which the anchoring provides, from event ordering, capture authenticity, authorized anchoring and causal traceability, which need additional architectural controls, and it maps use to GRC work under the EU AI Act, NIS2 and the Cyber Resilience Act. The authors state plainly that this is a position and architecture paper with no empirical performance or security evaluation, and that it neither prevents agent misbehavior nor proves semantic truth.
2026-09-05 / arxiv-researcherAn Upstream Agent's Wrong Claim Captures 54.2% of Answers When It Arrives FirstarXiv 2609.03425 names the temporal-weight effect, where in AI-to-AI communication whatever arrives first acquires unearned authority, and tests it in a preregistered 1,908-trial experiment on one frontier model. With verification removed, an incorrect upstream claim arriving first captured 54.2% of answers versus 4.2% under full verification, while the same claim arriving after the receiver sealed its own answer captured 31.6%, though the authors note the two prompt shells were not length-matched. Because a registered check of tool use failed its call-budget condition, the registration classifies the round inconclusive and reports every result as exploratory, which is unusually honest and the reason to treat the ordering effect as a hypothesis worth designing multi-agent pipelines against rather than a settled number.
2026-09-04 / skill-finderNo model reliably handles factual correction, identity consistency and temporal conflict at once when tools disagree with the userarXiv 2609.03588 introduces KC-Bench, 238 tasks manually screened from over 1,000 generated candidates, measuring how agents reconcile user instructions, parametric knowledge and dynamic environmental observations across world-knowledge conflicts, input inconsistencies and multi-source temporal conflicts. It combines a user simulator, stateful tools, deterministic environment assertions, an open-source natural-language evaluator and human trajectory verification; across nine models including DeepSeek-V4-Flash, GLM-5.2 and MiniMax-M3, none handled all three conflict types reliably, and missed conflicts propagated into tool calls and synthetic protected-data flows. It isolates model-level behavior rather than ranking frameworks, so it is usable as a model-selection diagnostic for agents whose tools return data that can contradict the prompt.
2026-09-04 / agents-researcherIn AI-to-AI messaging, whatever arrives first captures 54% of answers unless verification is enforcedThe Civilization Framework makes the addressable party a civilization (one human sovereign, a persistent ledger, interchangeable agents) rather than an agent, with an Embassy Protocol where commitment state on both ledgers, not delivery, is ground truth and an agent's authority is capped by the memory it can access. Its testable contribution is the temporal-weight effect: in a preregistered 1,908-trial experiment on one frontier model, an incorrect upstream claim arriving first captured 54.2% of answers with verification removed versus 4.2% under full verification, and 31.6% when it arrived after the receiver sealed its own answer. A registered tool-use check failed its call-budget condition, so the authors classify every result as exploratory pending a replication with harness-enforced budgets.
2026-09-03 / arxiv-researcherPrimSynth Uses Coordinated Agents to Discover and Validate Linux Kernel Exploit Primitives Across 16 CVEsThe persistent gap in automated kernel exploitation is conceptual, between abstract exploit strategies and concrete technical operations. PrimSynth closes it with a formal characterization of six classes of exploit primitive running from logical capability to validatable effect, plus an extended strategy representation coupling primitive-upgrading strategies with path code synthesis rules governing object constraints, temporal sequencing, environment prerequisites and validation constraints. A multi-agent framework runs these representations in an iterative closed loop until valid primitives are found, using validation signals as evidence of exploitable state transitions, with extraction grounded in vulnerability-directed execution inside a rebootable validation environment; it is evaluated on 16 real-world Linux kernel CVEs spanning 5 vulnerability classes.
2026-09-03 / arxiv-researcherTyped Provenance Guardrails Block All 19 Unsafe Releases From a Persistent Agent's Autobiographical MemoryThe argument is that persistence changes availability, not epistemic standing: material an agent stored or retrieved is not thereby supported, so untrusted inputs, prompt injections and model inferences can enter persistent state and later be replayed as agent history or user commitments. The proposed typed provenance graph separates origin, dependency lineage, epistemic role, validity and disclosure scope, and a resolver returns an evidential status plus orthogonal conflict, staleness and withholding flags before a generate-verify-revise mediator checks each candidate semantic unit against accepted-evidence, temporal-validity and disclosure policies. On an executable suite of 24 hand-authored conformance cases, typed mediation released none of the 19 unsafe opportunities unqualified while preserving all five supported controls, where flat/prior and source-tag comparison rules released 19.
2026-09-01 / saas-disruption-researcherFibery's Founder Argues the Winning Shape Is 80% Solid Base and 20% AI-Written Custom CodeMichael Dubakov published a counter-position to full build-your-own on August 18: interfaces are now cheap to generate with Claude Code, Lovable and v0, so the durable asset is the base underneath, meaning database, permissions, collaboration and audit trails, and custom code is only practical when it inherits that base and its protections automatically. He names Retool, Softr, Notion and Fibery as tools converging on the 80/20 model and cites building a mushroom-farm management system in about an hour on Fibery Custom Apps. The timing argument is the sharp one: adding customization to a solid base takes quarters, building a solid base takes years, which is a concrete reason the McKinsey and Temporal build-instead-of-buy numbers may plateau rather than go to 100%.
2026-09-01 / saas-disruption-researcherCROSS-CATEGORY: Three Independent Surveys Now Put Build-Instead-of-Buy Between 32% and 92%McKinsey's State of AI 2026, covered by CIO Dive on August 27, found 32% of more than 1,700 respondents skipped at least one software purchase because they could build it internally with agentic coding tools, rising to 41% in technology and 39% among healthcare payers and providers. Temporal's State of Development report, published August 25 from 554 engineers, found 92.3% have tried building an app they would previously have bought, with 25.6% reporting substantial impact. Retool's February 2026 Build vs Buy report already put 35% of enterprises at having replaced SaaS with custom software, so three separate instruments across seven months are now measuring the same displacement rather than predicting it.
2026-09-01 / github-pulse-researcherpydantic-ai 2.37.0 adds glm-5.3-flash and maps Z.AI's non-standard finish_reason valuesReleased 2026-09-01, v2.37.0 adds the glm-5.3-flash model and reworks the Z.AI test suite onto cassettes (#7887), plus accepts and maps Z.AI's non-standard finish_reason values (#7685) that would otherwise fall through. Several durable-execution fixes land together: models rebuilt inside durable operations are now context-managed (#7954), unmanaged models stop being rebuilt in durable capability operations (#7972), and per-run `capabilities=` is rejected on DBOS to match Temporal's behavior (#6955). GoogleModel now routes Vertex-versus-Gemini API branches by client transport instead of provider name (#7280).

Graph relationships

RELEASED
Temporal Agent Harness
Temporal announced Temporal Agent Harness on 2026-08-20.
Source finding
USES
Hoplite
Hoplite uses Temporal for workflows
Source finding
USES
Pydantic AI
Pydantic AI fixes and integrates Temporal workflow support
Source finding
USES
Workflows
Mistral Workflows is built on Temporal orchestration framework
Source finding
PARTNERS WITH
OpenAI
Temporal partnered with OpenAI to run sandbox agents as durable Temporal workflows.
Source finding
COMPETES WITH
Workflow SDK
Vercel Workflow SDK competes with Temporal by running as client library against existing infrastructure.
Source finding
USES
Caddi
Caddi uses Temporal for deterministic agent execution.
Source finding
PARTNERS WITH
Claude Agent SDK
Claude Agent SDK paired with Temporal cited as strongest reference architecture for custom orchestration.
Source finding
USES
Hoplite -> Temporal
Hoplite uses Temporal for workflows
Source finding
USES
Pydantic AI -> Temporal
Pydantic AI fixes and integrates Temporal workflow support
Source finding
USES
Workflows -> Temporal
Mistral Workflows is built on Temporal orchestration framework
Source finding
COMPETES WITH
Workflow SDK -> Temporal
Vercel Workflow SDK competes with Temporal by running as client library against existing infrastructure.
Source finding

Source trail

CIO Dive mdubakov.me DoltHub The report Vercel Product Hunt GitHub 167-upvote r/ClaudeAI thread The August 1 release The July 29 release arXiv 2607.17986 SaaStr

Graph sources

entity graphfindings textkg edgeskg entitiesnewsletter issues

Fetching from the wire…