Image created with Gemini. Image prompt: A 1983 Russell Mulcahy music video still in 16:9 of a colossal glowing gold medallion engraved with a bar chart eclipsed by a black disc with a blazing corona, hanging in the arched window of a fog-filled Victorian examination hall lined with empty judging desks and guttering candelabras, an anonymous woman in a luminous windblown white dress standing center-aisle as adult male fencers in white uniforms drift through smoke in frozen slow-motion, blinding white backlight, 35mm grain, soft diffusion bloom, and the bold chrome serif title ‘BENCHMARKS’ glowing across the top of the frame.
Muse Spark 1.1 is SOTA on Harvey’s Legal Bench, TaxEval, and MedScribe. It’s cool to see that our model outperforms even Fable in a few areas :)”
https://x.com/alexandr_wang/status/2075233663323947120
Meta is releasing an agentic and coding model that can compete with GPT-5.5 and Opus 4.8? And on top of that, it is SOTA on HLE? Holy, that was not on my bingo card. Alexandr Wang cooked. What is happening today!”
https://x.com/kimmonismus/status/2075232528726708245
Muse Spark 1.1 just launched and it’s their most capable coding agent model yet. On Terminal-Bench 2.1 it scores 80.0%, in the same cluster as Opus 4.8 (82.7%) and GPT 5.5 (83.4%). Use it in Cline with the Meta API!”
https://x.com/cline/status/2075271057326719152
OpenAI’s most anticipated model of the year is here, and it ranks #2 on Vals Index and Vals Multimodal Index. Although Fable 5 is still ahead on several benchmarks, GPT 5.6 is clearly in the same class, and is able to complete tasks like our CyberBench that Fable refuses.”
https://x.com/ValsAI/status/2075270642359029972
GPT-5.6 sets a new Terminal-Bench record at 91.9%. Priced the same as GPT 5.5 at $5/$30 per million tokens. With Fable getting pulled from Claude subscriptions and moving to API cost (~2x as expensive at $10/$50), we are glad to see @OpenAI’s commitment to accessibility.”
https://x.com/cline/status/2075278343927365991
GPT-5.6 Sol (max) scores similarly to Claude Fable 5 (max) in GDPval-AA v2, reflecting a similar ability to complete economically valuable tasks.”
https://x.com/ArtificialAnlys/status/2075268987550932998
GPT-5.6 Sol comes close second to Claude Fable 5 in the Artificial Analysis Intelligence Index at one third of the cost, and leads the Artificial Analysis Coding Agent Index in OpenAI’s Codex harness We supported @OpenAI with pre-release evaluation of GPT-5.6 Sol, Terra, and”
https://x.com/ArtificialAnlys/status/2075268970492657905
OpenAI just released GPT-5.6. But, instead of competing on benchmarks, they’re competing on cost curves. GPT-5.6 is doing something every frontier lab has been chasing: getting more work out of every token. It beats Fable 5 on coding-agent benchmarks while using less than half”
https://x.com/LiorOnAI/status/2075277748394967122
Big gains in PotatoMonetBench from Opus 4.5 to Fable: “There are two controls. One is slider, in which one side is labelled Maximum Potato and the other is Formalware, there are four positions. The other is a dial that goes from Monet to Drive-Thru.”
https://x.com/emollick/status/2074227397398794536
Elon Musk praises Mythos/Fable, promises not to ‘cut off’ Anthropic | TechCrunch
https://techcrunch.com/2026/07/09/elon-musk-praises-mythos-fable-promises-not-to-cut-off-anthropic/
SpaceXAI just released Grok 4.5, and it ranks #4 on GDPval-AA v2 with an Elo of 1543 – behind only the latest Claude releases from Anthropic on real-world agentic knowledge work tasks Grok 4.5 achieved this score at a cost of $0.49 per GDPval task to sit clearly on the Pareto”
https://x.com/ArtificialAnlys/status/2074942097158021371
GPT-5.6 – ARC-AGI Results
https://arcprize.org/results/openai-gpt-5-6
GPT-5.6 Sol sets a new SOTA on ARC-AGI-3: 7.8% Sol is the first verified frontier model to ever beat an ARC-AGI-3 game It is the best model at orienting in a situation it’s never encountered”
https://x.com/arcprize/status/2075270869992264003
While scores at the top of ARC-AGI v1/2 are topping out at 95% there is still a lot the benchmark tell us about efficiency Not only does GPT-5.6 Sol get 92.5% on ARC-AGI-2 (sota) but it does so at 1 OOM less cost than GPT-5.5 Pro(!) GPT-5.5 Pro came out 3 months ago…”
https://x.com/GregKamradt/status/2075274981794300113
After our announcement last month, Artificial Analysis is now launching Harvey LAB-AA (Legal Agent Benchmark), our implementation of Harvey’s new agentic legal benchmark that evaluates language models on real-world legal work across 24 practice areas Harvey LAB-AA tests models”
https://x.com/ArtificialAnlys/status/2074541975186165887
Meta Muse Video just entered the Video Arena at #3. @AIatMeta’s new video model scored 1459 in the Text-to-Video Arena. It outperforms Alibaba’s HappyHorse 1.0 by +30pts and ranks ahead of Grok Imagine, Sora 2 Pro and Google Veo-3.1 models. Meta has now reached the video AI”
https://x.com/arena/status/2074591193783320851
AI appears to be finding software vulnerabilities at scale. In June 2026, 21 notable organizations disclosed ~1,500 high- and critical-severity CVEs, over 3.5× the previous monthly record set before Claude Mythos Preview’s release.”
https://x.com/EpochAIResearch/status/2072776792809918604
xAI has just released Grok 4.5, beating Opus 4.8 on Terminal-Bench. Notably, Grok 4.5 is served at fast speeds w/ 2x greater token efficiency than other top models, while being priced ~5x cheaper than GPT. Meaning it delivers the highest intelligence per unit of time and cost.”
https://x.com/cline/status/2074928307427275108
1/ today, with the launch of muse image and the preview of muse video, we wanted to share more samples from the model alongside research details and eval results see some more samples from muse video below (and see thread for research blog!)”
https://x.com/alexandr_wang/status/2074579670712934428
// The Harness Effect // (bookmark it) Now more that ever pay very close attention to the orchestration harness and its effect on costs and performance. This study ran 22 evaluation tasks on six foundation models (Claude Sonnet 4.6, Gemini 3.1, Qwen 3.6, GLM 5.1, and others),”
https://x.com/dair_ai/status/2075241322655727682
The talk about Mythos and cybersecurity was not, in fact, hype. (As anyone using Fable to do autonomous work has probably recognized)”
https://x.com/emollick/status/2072778376494895139
Sol is #1 on our two newest benchmarks – CyberBench and Excel Modeling Benchmark (on Cyber, Fable had nearly a 100% refusal rate). It is also the new #1 on Legal Research Bench, ProofBench, SWE-bench, and Terminal-Bench 2.1, proving to be extremely strong on formal reasoning and”
https://x.com/ValsAI/status/2075270644711997581
1/ muse spark 1.1 is an industry-competitive agentic and coding model. across many agentic evals it rivals gpt-5.5 and opus-4.8. available now through the new meta model api and in meta ai. 🧵”
https://x.com/alexandr_wang/status/2075218936266998230
MAI-1 has no independent benchmarks yet, but the ones they released suggest it is worse than Sonnet 4.6. Not sure it is going to be great as a Copilot at Excel and Outlook, especially as there are already quite good Claude/OpenAI plugins for office using good AI. We will see.”
https://x.com/emollick/status/2074595359364411651
Big day of launches for OpenAI! New GPT-5.6 models: Sol, Terra, and Luna – Sol lands slightly below Fable on @ArtificialAnlys’ Intelligence Index, but beats it on several agentic coding benchmarks – Pricing lands the same as GPT 5.5, coming in significantly below Fable on”
https://x.com/TheRundownAI/status/2075273458661949763
On Agents’ Last Exam, GPT‑5.6 Sol sets a new high of 53.6, eclipsing Claude Fable 5 (adaptive) by 13.1 points. At medium reasoning, it beats Fable 5 by 11.4 points at roughly one-quarter the estimated cost. GPT‑5.6 Terra and Luna also outperforms Fable 5 at around one-sixteenth”
https://x.com/OpenAI/status/2075271423992680532
SpaceXAI’s Grok 4.5 scores 54 to place fourth on the Artificial Analysis Intelligence Index following only Fable 5, GPT-5.5, and Opus 4.8. It scores on par with GPT-5.5 in Codex on the Artificial Analysis Coding Agent Index in the Grok Build harness, at much lower cost Grok 4.5″
https://x.com/ArtificialAnlys/status/2074956932289282087
In addition to agents and coding, Muse Spark 1.1 is also really strong at answering health questions, a steadily growing use case for AI. On HealthBench-Pro, Muse Spark 1.1 achieves +5% better performance than Muse Spark 1.0 and beats all competitor models except Fable/Mythos.”
https://x.com/_jasonwei/status/2075265159430623334
Claude Sonnet 5 (Thinking) by @AnthropicAI debuts at #6 on the new Agent Arena leaderboard. In Agent Arena, we measure models on millions of real-world, long-horizon agentic tasks from a global community of users. Models can access web search, filesystem, and terminal tools to”
https://x.com/arena/status/2074484787663052849
Announcing AutomationBench-AA, our independent leaderboard for Zapier’s AutomationBench, testing whether AI agents can automate real SaaS workflows while adhering to business rules We partnered with @zapier to run AutomationBench-AA on their private benchmark subset. This”
https://x.com/ArtificialAnlys/status/2074194764510208230
We tuned the harness for @NVIDIAAI Nemotron 3 Ultra. Benchmark-leading performance. 10x lower inference costs. ✅ An aggregate score of 0.86 at a cost of $4.48 ✅ The closest-performing model: $43.48
https://x.com/LangChain/status/2074889720308339025
Introducing the NemoClaw Deep Agents Blueprint, a reference architecture for building open agent systems developed with @NVIDIA ✅ A fully open stack enterprises can own and customize ✅ Benchmark-leading performance ✅ Over 10x lower inference costs Blog:”
https://x.com/LangChain/status/2074871740350505309
Great writeup from the University of Oxford. It’s a taxonomy of LLM-based agent limitations. Good read for anyone shipping with agents. Benchmark scores keep climbing, yet the same agent failures resurface across otherwise unrelated evaluations, hidden behind the leaderboard.”
https://x.com/dair_ai/status/2074874153245814864
Paper page – PACE: A Proxy for Agentic Capability Evaluation
https://huggingface.co/papers/2607.02032
GPT-5.6 is here but also the long awayited Superapp the tl;dr The model side is impressive, but expected: GPT-5.6 Sol is strong across coding, browsing, cyber, science, long-context and agentic work. On several evals, OpenAI claims it either sets new SOTA or comes very close”
https://x.com/kimmonismus/status/2075271465964798147
ChatGPT Work is powered by GPT-5.6. GPT-5.6 makes ChatGPT state of the art at reasoning through complex tasks and creating materials that match your templates, reference files, and preferred style. Just describe the outcome you want, without having to spell out every step to”
https://x.com/OpenAI/status/2075274275104399670
We benchmarked coding agents on our own internal tasks at Databricks and learned a lot! There are many surprising opportunities to lower cost and increase quality, and many models including open source ones are truly competitive now. 🧵”
https://x.com/matei_zaharia/status/2074943612631273730
Grok 4.5 by @SpaceXAI is now in the Agent Arena! In Agent Arena, we measure models on millions of real-world, long-horizon agentic tasks from a global community of users. Models can access web search, filesystem, and terminal tools to complete complex workflows. The leaderboard”
https://x.com/arena/status/2074933774178300270
Duck Harness: Winning Solution for ARC-AGI-3 Milestone 1 | Tufa Labs
https://tufalabs.ai/research/duck-harness/
under official scoring methodology Sol would score 0% on ARC-AGI-3 as it was capped at $10k but for some reason Sol is now allowed to use $25k”
https://x.com/scaling01/status/2075279452494299273
Yesterday we launched SWE-1.7 built on the open-source Kimi K2.7. Concerns about Chinese base models are real: K2.7 completed 87% of tasks that other models refuse over human-rights concerns. We trained SWE-1.7 specifically on trustworthiness, so it matches US models on evals.”
https://x.com/cognition/status/2075308920755618144
Calibrating alignment evals — LessWrong
https://www.lesswrong.com/posts/mWpo4Tu87ZSFzwFWB/calibrating-alignment-evals
Grok 4.5 is SOTA on TB2.1… at reward hacking In all seriousness, even after zeroing out reward hacks, it is #4 on the TB2.1 leaderboard and lands on the Pareto for both cost and speed. (charts and reward hacking links in 🧵)”
https://x.com/alexgshaw/status/2075273675331580218
We’re introducing Provisioned Throughput: reserved inference capacity for frontier open models, with token-based pricing and a 99% uptime SLA. Serverless simplicity, guaranteed capacity, up to 90% lower cost vs. Opus 4.8. Get started with MiniMax M3 + GLM-5.2, read more 🧵”
https://x.com/togethercompute/status/2074756085785722930
You really need your own benchmarks. If you are translating hieroglyphics, use Gemini 3.5 Flash. If you are running a vending machine use Opus 4.8. (This is one reason why I am skeptical of just swapping out models to optimize costs or generic benchmarks without testing first)”
https://x.com/emollick/status/2072697436963832023
GLM-5.2 is close to Opus Grok 4.5 allegedly beats Opus and gpt-5.5 Meta even has something that’s supposedly close to gpt-5.5 Feels like it barely matters now”
https://x.com/theo/status/2074931078104805668
At Databricks, we built an internal coding benchmark on our multi-million-line codebase. Biggest takeaways: 1. Open-source coding models are no longer toys. GLM-5.2 is strong and cheaper. 2. OpenAI, Anthropic, and GLM-5.2 are all on the Pareto frontier. The real unlock is an”
https://x.com/Yuchenj_UW/status/2074956726940680558
Big news: Grok-4.5 has landed #3 in the Code Arena: Frontend. – On par with GLM-5.2 (Max) and Claude Opus 4.8 (Thinking) – Significant improvement over Grok-4.3 (#62 -> #3) – #2 for Content Creation Tools, Simulations, Gaming and Reference-Based Design – #3 for Consumer Product”
https://x.com/arena/status/2075301317560742373
Muse Spark 1.1 outperforms Opus 4.8 and Grok 4.5 on some nice out of distribution evals :)”
https://x.com/alexandr_wang/status/2075275671815999956
Seriously, what?! Did NOT expect grok 4.5 to perform THAT well. On par with GPT-5.5, cheaper than opus 4.8. Cursor deal paid off”
https://x.com/kimmonismus/status/2074915627307839511
SpeechifyAI’s Simba 3.2 takes the #1 spot on the Artificial Analysis Speech Arena Leaderboard, surpassing Sonic 3.5, Inworld Realtime TTS 1.5 Max and Google’s Gemini 3.1 Flash TTS Simba 3.2 is the latest TTS model from @SpeechifyAI. Key takeaways: ➤ Quality: Simba 3.2 has an”
https://x.com/ArtificialAnlys/status/2074265309985570890
Announcing the Controlled Voice Arena Leaderboard comparing Text to Speech models on the same set of 8 cloned voices The Controlled Voice Arena standardizes, through voice cloning, the set of voices that each model’s performance is evaluated on – separating specific voice”
https://x.com/ArtificialAnlys/status/2074886571166462405
okk something a bit different this time: thing I *don’t* like about this model – it thinks quite a bit, often very thorough when it really shouldn’t be; for direct execution i still use 1.6 thing i *do* like – I asked it to optimize Aerospace (the mac tiling window manager)”
https://x.com/jhanikhil/status/2074920413298393301
The last 24 hrs has seen 3-5 new pareto-frontier models (depending on how you count). I myself find it hard to keep up sometimes. The need for auto model routing has never been clearer.”
https://x.com/matanSF/status/2075276339607654802
Unsurprising but still big: MTurk is on its way out. Mechanical Turk was a mainstay of social & survey research through the 2010s, as it allowed you to quickly buy access to many representative humans. It was pretty good at it, until LLMs came along and everyone started using AI”
https://x.com/emollick/status/2074361301871083843
🚀 Turbocharged MiniMax’s MSA and NVIDIA’s GatedDeltaNet-2 (and new kernels for RTX PRO 6000 (SM120) support) over the weekend MSA previously required b200/b300 (tcgen05) but we ported it to SM120 (RTX Pro 6000) and made it much faster: – sparse-attn train 19-23ms → 2.9ms”
https://x.com/jon_durbin/status/2074119835366134188
The Epoch Capabilities Index now has slightly tighter confidence intervals, thanks to an update to the methodology we use to compute them.”
https://x.com/EpochAIResearch/status/2074916678094499992
Introducing EBR-bench, our new benchmark to measure on-the-fly learning. AI repeatedly plays a challenging board game called Earthborne Rangers and tries to learn from its mistakes. So far: no signs of improvement.”
https://x.com/EpochAIResearch/status/2072714372175237255
Google’s Nano Banana 2 Lite debuts at #5 on the Artificial Analysis Text to Image Leaderboard, slightly ahead of the base Nano Banana 2, but ranks substantially lower in Image Editing at #18 vs. Nano Banana 2’s #3 Nano Banana 2 Lite (Gemini 3.1 Flash Lite Image) is the newest”
https://x.com/ArtificialAnlys/status/2074917317591564619
https://t.co/k6Bcz1C0NT’s GLM-5.2 scores an estimated 152 on the Epoch Capabilities Index, the highest of any open-weight model we’ve evaluated. It remains behind models like Gemini 3 Pro, released over 7 months ago.”
https://x.com/EpochAIResearch/status/2074894535558300103
We’re launching the first in a series of Embodied Reasoning offerings: Perceptron Egocentric. It achieves SOTA over the best robotic annotation pipelines built on Gemini 3.5 Flash and Gemini Robotics-ER 1.6. Early access is available to partners.”
https://x.com/perceptroninc/status/2075261142038196727
Exciting news: Meta’s Muse Image just claimed #2 in the Image Arena! Muse Image from @AIatMeta now ranks second only to OpenAI’s GPT Image 2, outperforming Nano Banana, Grok Imagine, MAI Image, and many other leading image models. It holds #2 across the board: Text-to-Image,”
https://x.com/arena/status/2074581979765539153
SWE-1.7 was built on broad improvements in RL pipeline on top of a Kimi K2.7 base model. Our proprietary benchmark FrontierCode evaluates whether a model makes code you’d actually want to merge. SWE-1.7 advances the cost-performance Pareto curve on FrontierCode with a score of”
https://x.com/cognition/status/2074882970935034309
Added Grok 4.5 to my Surface Evolver benchmark. It’s on the frontier, achieving a better pass rate than kimi/glm at a similar price point.”
https://x.com/Deep_Burner/status/2074958078227951813
Quick summary of what happened during AWTF in Japan: – OpenAI crushed humans in both Heuristic and Algorithm categories. Heuristic is the one where I won in 2025 and OpenAI placed 2nd – AWTF is pretty much the highest level competitive programming contest: invited finalists in”
https://x.com/FakePsyho/status/2075291659814781370
new open (apache 2.0!) model from @TencentHunyuan, Hy3 is only 295B total 21B active and competitive with MUCH bigger model on benchmarks
https://x.com/eliebakouch/status/2074011171661701466
If these scores are real, Tencent has just become one of the leaders of open source. Hy3 is a 295B 21AB model that at least beats GLM 5.1 in blind tests. Might even warrant architecture transition (it’s a basic GQA). Good job.”
https://x.com/teortaxesTex/status/2074012467886178725
📊 A June logic benchmark says LLM evaluation is shifting from best-case retries to median reliability Here’s the evaluation from Zhihu contributor toyama nao. A long-running Chinese LLM logic benchmark just released its June 2026 update. The useful part is not only the ranking”
https://x.com/ZhihuFrontier/status/2074742636255306163
Exclusive: China’s MiniMax Plans to Launch 2.7-Trillion Parameter Model — The Information
https://www.theinformation.com/briefings/exclusive-chinas-minimax-plans-launch-2-7-trillion-parameter-model
Introducing six new Artificial Analysis Capability Indices for comparing model capabilities across key industry domains The new industry indices cover Finance & Accounting, Legal, Healthcare & Medical, Strategy & Ops, Engineering, and Economics. We aim to capture the common”
https://x.com/ArtificialAnlys/status/2074299714699469221
@OpenAI released GPT 5.6 today and we ran a day 0 benchmark in ParseBench to test improvements in document understanding. The new family of models continues to excel at reading text and tables, but continues to struggle with charts and layout. What’s most interesting is that”
https://x.com/llama_index/status/2075351095258296378
Muse Spark 1.1 is ranked #4 on the Vals Index, ahead of GPT-5.5 and Grok 4.5″
https://x.com/alexandr_wang/status/2075232956248100895
We comprehensively benchmarked GPT-5.6 on document understanding. At a high-level there’s no change between GPT-5.6 Sol and GPT-5.5 in terms of performance over tables, text, charts, layout, and more. The GPT-class of models typically does quite well over table understanding,”
https://x.com/jerryjliu0/status/2075356305099800717
GPT-5.6 Sol (max) is the new leader in CritPt, a benchmark of unpublished research-level physics problems CritPt, developed by Argonne and UIUC, tests models on graduate-level physics research problems contributed by 60+ researchers from 30+ institutions globally. GPT-5.6 Sol”
https://x.com/ArtificialAnlys/status/2075423964378366427
GPT-5.6 Sol (max) offers a minor improvement over GPT-5.5 in the AA-Omniscience Index, with a small uplift in accuracy coupled with an increase in hallucination rate vs GPT-5.5 (max).”
https://x.com/ArtificialAnlys/status/2075268990004605023
GPT-5.6 Sol defines a new Pareto frontier of Intelligence vs Output Tokens per Task in the Artificial Analysis Intelligence Index. Terra and Luna are not on the Pareto frontier.”
https://x.com/ArtificialAnlys/status/2075268984539410521
OpenAI’s GPT-4 led the Epoch Capabilities Index for 352 days after its March 2023 release, far longer than any model since. The second-longest lead belongs to OpenAI’s o1 at 98 days.”
https://x.com/EpochAIResearch/status/2072750485610893352
Separating signal from noise in coding evaluations | OpenAI
https://openai.com/index/separating-signal-from-noise-coding-evaluations/
The metrics discussion at OpenAI is a little confusing to me. I appreciate the clarification about bad benchmarks, but they spent a lot of money developing a very good benchmark of autonomous model ability at hard tasks, GDPval, and haven’t reported it for GPT-5.6.”
https://x.com/emollick/status/2075070920582943051
Token costs? GPT-5.6 Sol delivers the highest score at less than half the cost compared to competitors.”
https://x.com/omarsar0/status/2075270117131259925
We audited SWE-Bench Pro, one of the most widely used AI coding benchmarks, and found it no longer reliably measures frontier coding capability. We find 30% of SWE-Bench Pro tasks to be broken, and are retracting our previous recommendation that the research community use it as”
https://x.com/OpenAI/status/2074972179385720836
Today we release Antidoom, an open-source method that removes a common failure mode in reasoning models: the doom loop. Doom-loop rates before and after, with eval scores up across the board: > Early LFM2.5-2.6B checkpoint: 10.2% → 1.4% > Qwen3.5-4B: 22.9% → 1% (greedy”
https://x.com/liquidai/status/2074494130126811473
Artificial Analysis assessment”
https://x.com/elonmusk/status/2074948489792860456
if these benchmarks are representative of real world performance and pricing is only $6 output then it should do really well”
https://x.com/scaling01/status/2074914854473781707
Our new approach holds each model’s number of benchmark results fixed, then resamples that model’s scores with replacement. We refit all parameters on every bootstrap sample.”
https://x.com/EpochAIResearch/status/2074916702270455819
Reporting benchmark results as a scalar number, e.g. “75% on XYZ” is completely meaningless at this point. You should always report efficiency scores, e.g. “75% at a cost per task of $10.
https://x.com/fchollet/status/2074242671103889799
Separating signal from noise in coding evaluations | Hacker News
https://news.ycombinator.com/item?id=48837396
We’re hiring a Benchmark Engineer to join our Evaluations team! You’ll help expand our AI Benchmarking Hub – running and maintaining benchmarks, integrating with AI providers, and designing brand-new benchmarks from scratch.”
https://x.com/EpochAIResearch/status/2073165390927323532
We’re looking for new Researchers to join our Evaluations team! Help us curate real-world task suites, design rubrics, and evaluate how well frontier models handle open-ended tasks.”
https://x.com/EpochAIResearch/status/2073059949752803824
Grok 4.5 has insane value 4th best model on Artificial Analysis Index”
https://x.com/scaling01/status/2074957930533687471
Strong new SpaceXAI / Cursor release with Grok 4.5. Impressive benchmarks at a flash-level speed, efficiency, and affordable pricing.”
https://x.com/TheRundownAI/status/2074925814324301840
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