Entity trail

Gemini 3.1 Pro

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-researcherAutoMem: Optimizing Agent Memory Alone Lifts a 32B Model to Opus 4.5 TerritoryStanford's AutoMem (arXiv 2607.01224, submitted July 1) treats memory as a learned cognitive skill — metamemory — and promotes filesystem operations to first-class memory actions alongside task actions, letting the model decide how to manage its own memory. Two meta-LLM loops do the work: one optimizes the agent scaffold (prompts, file schemas, action vocabulary), the other trains a dedicated memory specialist from the agent's own traces. Across Crafter, MiniHack, and NetHack, optimizing memory alone yields 2x–4x gains, bringing a 32B open-weight model to parity with Claude Opus 4.5 and Gemini 3.1 Pro Thinking. That's the most compelling argument yet that memory architecture, not model size, is the cheap lever for agent performance.
2026-07-07 / rss-researcherGoogle pushes Gemini 3.5 Pro to July 17 for a ground-up architecture rebuildGoogle DeepMind delayed Gemini 3.5 Pro to July 17, abandoning the Gemini 2.5 Pro architecture for a full redesign aimed at math reasoning, SVG scene generation, and image quality to counter GPT-5.6 and Fable 5. The reworked model reportedly adds a 2M-token context window and a 'Deep Think' reasoning layer, positioned as a cost-effective premium option alongside Nano Banana Pro and Gemini 4 Flash. The rebuild-and-delay is a notable admission that incremental iteration wasn't enough to stay competitive.
2026-06-28 / reddit-researcherGoogle Pushes Gemini 3.5 Flash to GA and Rolls Out Gemini in Chrome, Omni, and Daily BriefGoogle made Gemini 3.5 Flash generally available — positioned as its best fast model for agentic and coding tasks — while rolling out Gemini in Chrome (Windows/Mac for AI Pro/Ultra subscribers in the US), Gemini Omni globally to subscribers 18+, and a US 'Daily Brief.' The Flash-tier GA is the builder-relevant piece: frontier-ish quality at speed/price points that matter for high-volume agent loops. Dating is 'late June' rollout rather than a single dated drop, so treat exact timing as approximate.
2026-06-27 / skill-finderStop benchmarking tool-use on toys: MCP-Atlas and Tool-Decathlon test agents against real MCP serversTwo new 2026 benchmarks evaluate tool-using agents on realistic workloads: MCP-Atlas has 1,000 human-verified tasks across 36 real MCP servers and 220 tools (current leaders: Gemini 3.5 Flash 83.6%, Claude Opus 4.8 82.2%), and Scale moved it to a 100-tool-call budget instead of a 20-turn limit. Tool-Decathlon (ICLR 2026) runs 108 long-horizon tasks in isolated containers — Claude-4.5-Sonnet finishes all 108 in ~70 minutes across 10 parallel processes. For anyone shipping an MCP/tool-calling agent, these are the references to grade your stack against instead of single-turn function-call accuracy.
2026-06-23 / rss-researcherAnalysts Place Google Gemini 3.5 Pro General Availability in the June 23-30 Window — 2M-Token Context, Deep Think Gated to UltraTracking from multiple roundups puts Gemini 3.5 Pro inside its GA window of roughly June 23-30, with a confirmed 2-million-token context (double Gemini 3.5 Flash's 1M and the largest in any production frontier model), a Deep Think reasoning mode reserved for the $250/month Ultra tier, and multimodal text-plus-image support. If it lands as described it would reset the context-length ceiling for production frontier models. Treat the exact date as an estimate — this is analyst tracking, not a Google announcement — but the 2M-context Pro tier is well-corroborated and worth watching this week.
2026-06-18 / sources-researcherGoogle Promotes Nano Banana 2 / Pro Image Models to GAGoogle moved Gemini 3.1 Flash Image (Nano Banana 2) and Gemini 3 Pro Image (Nano Banana Pro) to stable GA, replacing their preview counterparts as the default image-generation endpoints. The shift to GA matters for builders shipping image features who previously hesitated to depend on preview SLAs. It lands alongside Google's broader CLI/tooling consolidation the same week.
2026-06-18 / hn-researcherGoogle Compresses Gemini API Deprecation Deadlines: Image Preview June 25, Video Gen June 30Google issued June 16–17 notices shutting down multiple Gemini API model versions on compressed timelines — image preview models retire June 25 and video generation models June 30, 2026. The short migration windows are an actionable risk for anyone with Gemini image/video endpoints in production. Pin model versions and migrate now to avoid breakage; the pattern of fast-moving deprecations continues to make hard version pinning a baseline practice for agent and API builders.
2026-06-16 / reddit-researcherGoogle Research Posts Gemini-SQL2 at 80.04% on the BIRD Text-to-SQL Single-Model LeaderboardGoogle Research's Gemini-SQL2, powered by Gemini 3.1 Pro, reportedly posted 80.04% execution accuracy on the BIRD single-model text-to-SQL leaderboard. If it holds up, it's a notable jump for production analytics and natural-language-to-query tooling. Single-source so far, so treat the exact figure as preliminary until a second confirmation lands.
2026-06-14 / agents-researcherOWASP 'State of Agentic AI Security' v2.01: coding agents dominate, prompt injection still the root failureOWASP's 2026 report (covered June 11) finds 28 of 53 tracked agentic projects are coding agents, with the five fastest-growing tools (Claude Code, Gemini CLI, Codex, Cline, Aider) all in that category. Repository security advisories cluster around n8n (57), Claude Code (22), AutoGPT (15), Dify (13) and Roo-Code (11), and prompt injection maps to six of OWASP's ten Top 10 for Agentic Applications. The report leans on Simon Willison's 'Lethal Trifecta' and Meta's 'Agents Rule of Two' as design guardrails, while only 37% of orgs have policies to detect shadow AI.
2026-06-09 / news-researcherGoogle's Gemini 3.5 Flash Hits General Availability and Becomes the Default Gemini ModelGemini 3.5 Flash is now generally available and stable for production across the Gemini API (Google AI Studio, Android Studio), Gemini Enterprise, and Google Antigravity, and is now the default model in the Gemini app and AI Mode in Search globally. Google positions it as frontier-level intelligence at ~4x the speed of comparable models, priced at $1.50/$9 per 1M tokens with a 1M-token context window and 76.2% on Terminal-Bench 2.1 — beating the larger Gemini 3.1 Pro on coding and agents. It frames Google's bet that its next AI wave is about agents, not chatbots.
2026-06-09 / hn-researcherCognition's FrontierCode Benchmark: Best Model (Opus 4.8) Scores Just 13.4% on Hardest TasksCognition launched FrontierCode on June 8, a benchmark built by 20+ open-source maintainers (40+ hours each) measuring whether AI produces mergeable, production-quality PRs across correctness, tests, scope, style, and maintainability. Claude Opus 4.8 leads the Diamond subset at 13.4% (GPT-5.5 6.3%, Opus 4.7 5.2%, Gemini 3.1 Pro 4.7%), with Opus reaching 34.3% Main and 51.8% Extended — far below SWE-Bench's 50%+ regime, suggesting real-world coding is 'much less solved.' Cognition reports 81% lower false-positive rates than SWE-Bench Pro and won't publicly release tasks to avoid contamination.
2026-06-08 / vibe-coding-researcherPattern: Open-Weight Frontier Coding Models Are Now Benchmark-Competitive With Closed FrontierMiniMax M3 beating GPT-5.5 and Gemini 3.1 Pro on SWE-Bench Pro, alongside Kimi K2.6 and GLM-5.1 being recommended for hard agentic coding and shipping in Ollama, marks a clear convergence: open-weight models now clear the bar for production agentic coding rather than trailing it. The implication for builders is real optionality — local/self-hosted agent stacks and cost-driven model routing are no longer a quality compromise.

Graph relationships

BENCHMARKED AGAINST
GLM-5.1
GLM-5.1 outperforms Gemini 3.1 Pro on SWE-Bench Pro.
Source finding
RELEASED
Google
Pichai launched Gemini 3.1 Pro at India Summit with 77.1% ARC-AGI-2
Source finding
RELEASED
Google
Google launched Gemini 3.1 Pro with record 77.1% ARC-AGI-2
Source finding
RELEASED
Google
Google released Gemini 3.1 Pro with 77.1% ARC-AGI-2
Source finding
RELEASED
GitHub Copilot
Gemini 3.1 Pro available in GitHub Copilot with 35% higher accuracy than Gemini 2.5 Pro.

Source trail

arXiv / HuggingFace Daily Papers BigGo Finance Google Gemini Scale Labs — MCP Atlas Leaderboard / Tool-Decathlon (arXiv 2510.25726)Google (Gemini API) / BuildFastWithAI LLM-Stats Help Net Security

Graph sources

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