[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-dario-ai-exponential-ending-soon-agi-in-years-summary":3,"summaries-facets-categories":135,"summary-related-dario-ai-exponential-ending-soon-agi-in-years-summary":4541},{"id":4,"title":5,"ai":6,"body":13,"categories":113,"created_at":114,"date_modified":114,"description":105,"extension":115,"faq":114,"featured":116,"kicker_label":114,"meta":117,"navigation":118,"path":119,"published_at":120,"question":114,"scraped_at":114,"seo":121,"sitemap":122,"source_id":123,"source_name":124,"source_type":125,"source_url":126,"stem":127,"tags":128,"thumbnail_url":114,"tldr":132,"tweet":114,"unknown_tags":133,"__hash__":134},"summaries\u002Fsummaries\u002Fdario-ai-exponential-ending-soon-agi-in-years-summary.md","Dario: AI Exponential Ending Soon, AGI in Years",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","x-ai\u002Fgrok-4.1-fast",9108,2184,22745,0.00242355,{"type":14,"value":15,"toc":104},"minimark",[16,21,25,28,31,35,38,41,44,48,51,54,57,61,77,81],[17,18,20],"h2",{"id":19},"scaling-hypothesis-holds-across-pre-training-and-rl","Scaling Hypothesis Holds Across Pre-Training and RL",[22,23,24],"p",{},"Dario Amodei reaffirms his 2017 \"Big Blob of Compute Hypothesis,\" arguing that raw compute, data quantity\u002Fquality, training duration, scalable objectives like pre-training or RL, and numerical stability drive progress over clever techniques. He references Rich Sutton's \"Bitter Lesson\" and notes pre-training scaling laws continue delivering gains, now extending to RL phases post-pre-training.",[22,26,27],{},"Amodei observes log-linear RL improvements on tasks like math contests (AIME) and code, mirroring pre-training. \"We’re seeing the same scaling in RL that we saw for pre-training,\" he states. This counters skeptics like Sutton, who question scaling's validity for human-like learning due to poor sample efficiency—trillions of tokens vs. human exposure. Amodei frames pre-training as a hybrid between human evolution (priors) and lifetime learning, with in-context learning bridging short- and long-term adaptation. Humans start with evolved brain structures; LLMs from random weights, explaining data hunger but enabling broad generalization from internet-scale scrapes like Common Crawl.",[22,29,30],{},"Dwarkesh Patel probes why build RL environments for skills like API use or Slack if in-context agents emerge. Amodei clarifies: RL mirrors pre-training's broad exposure for generalization, not exhaustive skill coverage—e.g., GPT-2 generalized to linear regression from diverse text, unseen before.",[17,32,34],{"id":33},"nearing-the-end-of-the-exponential-timelines-and-confidence","Nearing the End of the Exponential: Timelines and Confidence",[22,36,37],{},"Three years post their last talk, Amodei says capabilities progressed as expected—from high-school to PhD-level, exceeding in code—but the shock is public underreaction. \"The most surprising thing has been the lack of public recognition of how close we are to the end of the exponential,\" he says. \"It is absolutely wild... people talking about the same tired, old hot-button political issues, when we are near the end of the exponential.\"",[22,39,40],{},"He pegs 90% odds on a \"country of geniuses in a data center\" within 10 years: verified tasks (coding, math) in 1-2 years, near-certainty barring black swans like Taiwan invasion disrupting fabs. For non-verifiable tasks (Mars planning, CRISPR discovery, novels), generalization from verified domains already shows promise, though a spectrum of progress across domains persists. By 2035, full AGI seems mainstream-inevitable in sane scenarios.",[22,42,43],{},"Patel pushes on uneven frontiers and human-like uniformity. Amodei concedes potential splits but bets on spillover: models already generalize from verifiable RL to unverified. On software engineering, he distinguishes weak metrics (90% AI-written lines, already happening at Anthropic) from strong (100% end-to-end tasks: compiling, testing, memos). Even 100% task automation won't eliminate SWEs—new abstractions emerge, boosting productivity beyond line counts, akin to compilers.",[17,45,47],{"id":46},"economic-diffusion-compute-strategy-and-rl-needs","Economic Diffusion, Compute Strategy, and RL Needs",[22,49,50],{},"Patel questions if RL scaling implies diffusion cope or if continual learning is essential. Amodei views post-training RL as key for agentic capabilities, with broad RL data enabling generalization like pre-training did. Continual learning remains unsolved but unnecessary short-term; long contexts (1M tokens) already yield strong in-context adaptation.",[22,52,53],{},"On compute: If AGI imminent, why not hoard more? Amodei implies Anthropic balances timelines with risks, though not explicit. Labs' profitability: Frontier models commoditize, but value accrues via infrastructure, custom RL pipelines, and enterprise agents. Regulations risk stifling boons—Amodei warns of overreach destroying gains. US-China: Can't both dominate; compute bottlenecks favor leaders, but cooperation possible.",[22,55,56],{},"He shares anecdotes: GPT-1's narrow fanfiction training failed generalization; GPT-2's Reddit\u002FCommon Crawl scrape unlocked patterns like regression. Eight months ago, predicted 90% AI code lines in 3-6 months—verified at Anthropic.",[17,58,60],{"id":59},"quotes-capturing-counterintuitive-views","Quotes Capturing Counterintuitive Views",[62,63,64,68,71,74],"ul",{},[65,66,67],"li",{},"\"What has been the most surprising thing is the lack of public recognition of how close we are to the end of the exponential. To me, it is absolutely wild...\" —Dario Amodei, highlighting societal blind spots amid rapid progress.",[65,69,70],{},"\"Pre-training is not like the process of humans learning, but it’s somewhere between the process of humans learning and the process of human evolution.\" —Amodei, reframing data inefficiency as evolutionary mimicry.",[65,72,73],{},"\"90% of code is written by the model, 100% of code is written by the model. That’s a big difference in productivity.\" —Amodei, distinguishing weak from transformative SWE automation metrics.",[65,75,76],{},"\"On the ten-year timeline I’m at 90%, which is about as certain as you can be. I think it’s crazy to say that this won’t happen by 2035.\" —Amodei on AGI inevitability.",[17,78,80],{"id":79},"key-takeaways","Key Takeaways",[62,82,83,86,89,92,95,98,101],{},[65,84,85],{},"Bet on scaling: Prioritize compute, broad high-quality data, long training, and stable objectives over novel methods—pre-training and RL both log-linear.",[65,87,88],{},"Generalization emerges from diversity: Train on internet-scale or multi-task RL for spillover to novel skills, like GPT-2's unseen regression.",[65,90,91],{},"Timelines: Expect coding automation (end-to-end) in 1-2 years; genius-level AI systems in ~10 years (90% odds), even for creative tasks via verified generalization.",[65,93,94],{},"Productivity spectra matter: AI writing 90% lines ≠ 90% fewer engineers; full task automation unlocks new abstractions.",[65,96,97],{},"Public urgency needed: Exponential nears end—ignore politics, prepare for economic upheaval from AI diffusion.",[65,99,100],{},"RL ≠ human learning: View as broad capability builder, not skill drill; in-context handles on-the-fly adaptation.",[65,102,103],{},"Risks: Geopolitics (Taiwan), regulation could delay; labs must innovate beyond models for profits (agents, infra).",{"title":105,"searchDepth":106,"depth":106,"links":107},"",2,[108,109,110,111,112],{"id":19,"depth":106,"text":20},{"id":33,"depth":106,"text":34},{"id":46,"depth":106,"text":47},{"id":59,"depth":106,"text":60},{"id":79,"depth":106,"text":80},[],null,"md",false,{},true,"\u002Fsummaries\u002Fdario-ai-exponential-ending-soon-agi-in-years-summary","2026-04-08 21:21:17",{"title":5,"description":105},{"loc":119},"3381c5b10e177d61","Dwarkesh Patel","article","https:\u002F\u002Funknown","summaries\u002Fdario-ai-exponential-ending-soon-agi-in-years-summary",[129,130,131],"llm","machine-learning","research","Dario Amodei sees scaling laws holding for pre-training and RL, predicts 'country of geniuses' in data centers within 10 years (90% confident), coding automation in 1-2 years, 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In the dominant 'strong superposition' regime, every token gets represented, and error stems from overlap noise, not dropped rare tokens. Doubling model width (m) halves error via the geometric 1\u002Fm relationship, yielding power-law scaling (exponent ~1) regardless of data distribution. Weak superposition, where only common tokens are stored cleanly, requires power-law token frequencies for scaling—less reliable for natural language's flatter distributions.",[22,4560,4561],{},"This mechanistic view outperforms prior assumptions: real LLMs don't discard rare tokens but overlap everything, matching theory with measured overlap strength shrinking at 1\u002Fm.",[17,4563,4565],{"id":4564},"validation-across-real-models-matches-theory","Validation Across Real Models Matches Theory",[22,4567,4568],{},"Analysis of output layers in OPT, GPT-2, Qwen2.5, and Pythia (100M to 70B parameters) confirms strong superposition: all tokens represented with overlaps scaling at 1\u002Fm. Observed exponent of 0.91 aligns with theory's 1; DeepMind's Chinchilla data hits 0.88. Simplified models toggling overlap regimes prove scaling emerges directly from geometry, not just data power laws ('power law in, power law out').",[17,4570,4572],{"id":4571},"limits-and-optimization-opportunities","Limits and Optimization Opportunities",[22,4574,4575],{},"Scaling halts when width equals vocabulary size—no more overlaps needed, error from superposition vanishes, breaking power laws. Natural language's even frequencies limit speedup, but uneven domains (e.g., specialized vocab) enable steeper curves. Architectures promoting denser packing, like Nvidia's nGPT (vectors on unit sphere), boost performance at fixed size. Trade-off: denser overlaps hinder mechanistic interpretability, complicating AI safety.",{"title":105,"searchDepth":106,"depth":106,"links":4577},[4578,4579,4580],{"id":4554,"depth":106,"text":4555},{"id":4564,"depth":106,"text":4565},{"id":4571,"depth":106,"text":4572},[],{"content_references":4583,"triage":4599},[4584,4590,4594],{"type":4585,"title":4586,"author":4587,"url":4588,"context":4589},"paper","Toy Model of Superposition","Anthropic","https:\u002F\u002Ftransformer-circuits.pub\u002F2022\u002Ftoy_model\u002Findex.html","cited",{"type":4585,"title":4591,"author":4592,"url":4593,"context":4589},"Chinchilla","DeepMind","https:\u002F\u002Fthe-decoder.com\u002Fdeepmind-artificial-intelligence-is-far-from-being-fed-up\u002F",{"type":4585,"title":4595,"author":4596,"url":4597,"context":4598},"nGPT","Nvidia","https:\u002F\u002Farxiv.org\u002Fabs\u002F2410.01131","mentioned",{"relevance":4600,"novelty":4601,"quality":4601,"actionability":106,"composite":4602,"reasoning":4603},3,4,3.25,"Category: AI & LLMs. The article discusses the mechanics of LLM scaling through strong superposition, which is relevant to AI engineering. It presents new insights into how model width affects prediction error, but lacks practical applications or frameworks that the audience can directly implement.","\u002Fsummaries\u002Fllm-scaling-works-via-strong-superposition-summary","2026-05-03 08:42:45","2026-05-03 17:01:29",{"title":4544,"description":105},{"loc":4604},"5c8a61f1aa3cea08","The Decoder","https:\u002F\u002Fthe-decoder.com\u002Fmit-study-explains-why-scaling-language-models-works-so-reliably\u002F","summaries\u002Fllm-scaling-works-via-strong-superposition-summary",[129,130,131],"LLMs pack all tokens into limited dimensions via overlapping vectors (strong superposition), causing prediction error to halve when model width doubles—explaining reliable power-law scaling.",[],"GnbVczssaMG6qPbbjD4Uv1b3pkml1ZtHy5ldG-1N-44",{"id":4618,"title":4619,"ai":4620,"body":4625,"categories":4667,"created_at":114,"date_modified":114,"description":105,"extension":115,"faq":114,"featured":116,"kicker_label":114,"meta":4668,"navigation":118,"path":4685,"published_at":114,"question":114,"scraped_at":4686,"seo":4687,"sitemap":4688,"source_id":4689,"source_name":124,"source_type":125,"source_url":4690,"stem":4691,"tags":4692,"thumbnail_url":114,"tldr":4693,"tweet":114,"unknown_tags":4694,"__hash__":4695},"summaries\u002Fsummaries\u002Fllm-pretraining-scaling-fsdp-wins-until-comms-crat-summary.md","LLM Pretraining Scaling: FSDP Wins Until Comms Crater",{"provider":7,"model":8,"input_tokens":4621,"output_tokens":4622,"processing_time_ms":4623,"cost_usd":4624},8296,2378,19998,0.00282555,{"type":14,"value":4626,"toc":4661},[4627,4631,4634,4637,4641,4644,4648,4651,4655,4658],[17,4628,4630],{"id":4629},"fsdp-dominates-parallelism-until-scale-forces-pipeline-trade-offs","FSDP Dominates Parallelism Until Scale Forces Pipeline Trade-offs",[22,4632,4633],{},"Pretraining FLOPs = 6ND (2 forward + 4 backward per param-token). Data parallel (DP) copies weights across GPUs but hits HBM limits (B300: 288GB). Fully Sharded Data Parallel (FSDP) shards params per layer across GPUs, all-gathering full weights per layer (forward\u002Fbackward) while overlapping comms with compute since weights are layer-independent. FSDP comms: params×3 (all-gather forward\u002Fback + reduce-scatter backward), 50% over DP's params×2 all-reduce—achievable because all-gather is half an all-reduce. Use hierarchical collectives across NVLink domains: reduce-scatter intra-domain, all-reduce shards inter-domain, all-gather intra-domain to saturate IB bandwidth.",[22,4635,4636],{},"Comms time stays flat with GPU count (ring all-reduce chunks scale inversely with participants), but compute drops linearly, cratering MFU at 'crossover' (comms > compute). Delay crossover by larger batches (more compute\u002FGPU) or sparser models; TPUs excel with bigger domains. Batch size floors FSDP at ~1K GPUs (e.g., 10M-token batch, 10K seq len = 1K seqs). Add pipeline parallelism (PP) next, but it introduces bubbles (idle GPUs at batch start\u002Fend) unfillable in training due to per-batch gradient sync. PP constrains architecture (e.g., Kimi's cross-layer attention, mixed attention types cause stage imbalance), slowing research.",[17,4638,4640],{"id":4639},"distillation-remains-cheap-and-evasion-proof","Distillation Remains Cheap and Evasion-Proof",[22,4642,4643],{},"Frontier labs can't halt distillation: 1T tokens from Opus 4.6 costs $25M ($25\u002FMTok), commoditizing open models rapidly (cf. Fineweb 18.5T, OpenWebText 9B). Hiding chain-of-thought (CoT) fails—instruct no-think\u002Fdirect solve or RLVR on reconstructed CoT. Core value in local tool use (file edits, bash) evades cloud hiding; users resist workflow migration. Products atop APIs distill better: reward 'gold diffs' (final user-accepted code) over rejected intermediates from 10+ turn sessions.",[17,4645,4647],{"id":4646},"agentic-ai-shifts-cybersecurity-toward-defense","Agentic AI Shifts Cybersecurity Toward Defense",[22,4649,4650],{},"Mythos chains 5+ vulns into exploits (vs. prior single-vuln finds), but software is securer now despite human probing—sudden AI intelligence influx likely strengthens defense via industry patching (e.g., Glasswing reveals zero-days). AI excels at vuln finding over patching (XKCD: fixes break edge cases\u002Ffeatures). Solutions: LLM-port C to Rust; formal verification (e.g., seL4 proofs); patching mirrors LLM bug-finding in others' repos. Hoarding Mythos risky—build\u002Frelease classifiers rejecting cyberattack intents (Anthropic plans for 4.7). Evade classifiers by subproblems (harmless vulns). Patching own code routine for coding LLMs.",[17,4652,4654],{"id":4653},"pipeline-rl-fixes-stragglers-causalitybias-dooms-runs","Pipeline RL Fixes Stragglers; Causality\u002FBias Dooms Runs",[22,4656,4657],{},"RL responses grow in mean\u002Fvariance length, straggling GPU utilization. Pipeline RL does 'in-flight weight updates': swap generating model mid-trajectory post-training step, ensuring recent-model rollouts without full offline RL off-policyness.",[22,4659,4660],{},"Pretraining fails via causality breaks (MoE expert-choice routes token n+k affecting n; token-dropping ignores early for later matches—rumored Llama 4\u002FGemini 2 flops) or bias (FP16 collectives round large sums wrong, e.g., post-1024 granularity skips +1; GPT-4 initial bug). Bias compounds > variance. New scale unveils bespoke issues (numerics, kernels)—not 5 fixable failure modes. RL inference needs training-engine fidelity (numerical drift biases); enforce disciplined compute multipliers to avoid bug stacks. Kernel optimization AGI-hard (Nvidia took ages for Blackwell).",{"title":105,"searchDepth":106,"depth":106,"links":4662},[4663,4664,4665,4666],{"id":4629,"depth":106,"text":4630},{"id":4639,"depth":106,"text":4640},{"id":4646,"depth":106,"text":4647},{"id":4653,"depth":106,"text":4654},[],{"content_references":4669,"triage":4682},[4670,4674,4677],{"type":4671,"title":4672,"url":4673,"context":4598},"podcast","Conversation with Michael Nielsen","https:\u002F\u002Fwww.dwarkesh.com\u002Fp\u002Fmichael-nielsen",{"type":4585,"title":4675,"url":4676,"context":4589},"Pipeline RL","https:\u002F\u002Farxiv.org\u002Fpdf\u002F2509.19128",{"type":4678,"title":4679,"author":4680,"url":4681,"context":4598},"other","Pretraining parallelisms lecture","Horace He","https:\u002F\u002Fhorace.io\u002F",{"relevance":4601,"novelty":4600,"quality":4601,"actionability":106,"composite":4683,"reasoning":4684},3.4,"Category: AI & LLMs. The article discusses the practical application of Fully Sharded Data Parallel (FSDP) for scaling pretraining in LLMs, which addresses a specific pain point for AI developers regarding efficient model training. However, while it provides technical insights, it lacks concrete actionable steps that the audience could directly implement.","\u002Fsummaries\u002Fllm-pretraining-scaling-fsdp-wins-until-comms-crat-summary","2026-04-19 01:22:25",{"title":4619,"description":105},{"loc":4685},"d445780e74d7b6ed","https:\u002F\u002Fwww.dwarkesh.com\u002Fp\u002Fwhat-i-learned-april-15","summaries\u002Fllm-pretraining-scaling-fsdp-wins-until-comms-crat-summary",[129,130,131],"Use FSDP as default for scaling pretraining (params×3 comms overhead) until GPU count hits comms crossover; distillation costs $25M\u002FT from frontier models, unstoppable via tool use; training fails from causality breaks and FP16 bias.",[],"mv5ehBrGvWj0mxNgvs8TDhy7_ksYMEPU-s8KOMpXQwI",{"id":4697,"title":4698,"ai":4699,"body":4704,"categories":4790,"created_at":114,"date_modified":114,"description":105,"extension":115,"faq":114,"featured":116,"kicker_label":114,"meta":4791,"navigation":118,"path":4809,"published_at":4810,"question":114,"scraped_at":4811,"seo":4812,"sitemap":4813,"source_id":4814,"source_name":4815,"source_type":125,"source_url":4816,"stem":4817,"tags":4818,"thumbnail_url":114,"tldr":4820,"tweet":114,"unknown_tags":4821,"__hash__":4822},"summaries\u002Fsummaries\u002Fvisual-primitives-solve-lmm-reference-gap-summary.md","Visual Primitives Solve LMM Reference Gap",{"provider":7,"model":8,"input_tokens":4700,"output_tokens":4701,"processing_time_ms":4702,"cost_usd":4703},8697,2172,36756,0.00280275,{"type":14,"value":4705,"toc":4784},[4706,4710,4713,4733,4736,4740,4743,4746,4749,4753,4756,4759,4762,4765,4768,4771,4774,4778,4781],[17,4707,4709],{"id":4708},"embed-coordinates-as-core-reasoning-units-to-eliminate-reference-gap","Embed Coordinates as Core Reasoning Units to Eliminate Reference Gap",[22,4711,4712],{},"Current large multimodal models (LMMs) suffer from a 'Reference Gap': natural language can't precisely pinpoint visual entities, causing failures in dense counting, multi-step spatial reasoning, and tracking. For example, asking 'What is the leftmost bird doing?' among 50 birds forces vague descriptions like 'gray bird near left edge,' collapsing logic chains.",[22,4714,4715,4716,4720,4721,4724,4725,4728,4729,4732],{},"DeepSeek's solution elevates bounding boxes (",[4717,4718,4719],"span",{},"x1,y1,x2,y2",") and points (",[4717,4722,4723],{},"x,y",") from final outputs to 'visual primitives'—minimum units of thought. The model outputs coordinates inline during reasoning: 'I see a ",[4717,4726,4727],{},"452,23,804,411"," climbing a tree (exclude); ",[4717,4730,4731],{},"50,447,647,771"," on ground (include).' This anchors every step visually, mimicking human pointing while scanning, preventing lost tracks in dense scenes.",[22,4734,4735],{},"Built on DeepSeek-V4-Flash with DeepSeek-ViT vision encoder in LLaVA-style architecture (ViT features + LLM), it follows standard fusion but innovates in reasoning paradigm.",[17,4737,4739],{"id":4738},"achieve-7056x-token-compression-with-no-capability-loss","Achieve 7056x Token Compression with No Capability Loss",[22,4741,4742],{},"Processing an 800x800 image yields 2,916 patch tokens, bloating KV cache and slowing inference. DeepSeek applies two-stage compression: spatial (3x3 patches to 1 token, 2,916 → 324) + DeepSeek-V4-Flash's 4x Compressed Sparse Attention (324 → 81 tokens, ~90 KV slots total). Result: 7056x overall compression.",[22,4744,4745],{},"Comparisons: Gemma-4-31B (289 tokens), GPT-4o (740? note: likely GPT-4 variants), Claude-3.5-Sonnet (870? labeled 4.6), Gemini-1.5-Flash (1,100). DeepSeek uses 1\u002F10th of Claude's tokens.",[22,4747,4748],{},"Performance holds: 77.2% average across 7 benchmarks (counting, spatial reasoning, maze navigation, path tracking), beating GPT-4o (71.1%), Claude-3.5-Sonnet (65.3%), Gemini-1.5-Flash (76.5%). Excels in multi-step tasks: maze navigation 66.9% (vs GPT-4o 50.6%), path tracking 56.7% (vs 46.5%), Pixmo-Count 89.2% (vs Gemini 88.2%), fine-grained counting 88.7% (vs Qwen2-VL 87.2%).",[17,4750,4752],{"id":4751},"five-step-training-pipeline-yields-unified-spatial-expert","Five-Step Training Pipeline Yields Unified Spatial Expert",[22,4754,4755],{},"Pre-training: Crawl 97,984 bounding box sources (HuggingFace etc.), filter via Semantic Review (MLLM checks labels for nonsense\u002Fambiguity\u002Fharm) + Geometric Review (valid framing, no truncation\u002Fgiant boxes >90% area), retaining 31,701 sources → 40M+ samples.",[22,4757,4758],{},"SFT: Train separate box\u002Fpoint experts to avoid conflicts on small data.",[22,4760,4761],{},"RL: GRPO with 3 rewards—format (correct syntax, no duplicates\u002Floops), quality (LLM-judged reasoning), accuracy (task-specific: counting reward = 1 \u002F (1 + |pred - gt| \u002F (gt + 1)) with α=0.7, β=3 for dense tolerance; maze: causal progress ratio + completeness, truncating illegal wall-passes).",[22,4763,4764],{},"Rejection Fine-Tuning: Merge experts.",[22,4766,4767],{},"On-Policy Distillation: Experts teach student via full-vocab logits + reverse KL (peaks multimodal distributions, cuts hallucinations).",[22,4769,4770],{},"Evaluations span counting (coarse\u002Ffine-grained, anchor per object), spatial reasoning (multi-hop\u002Fembodied), mazes (grids\u002Fcircles\u002Fhoneycombs, including unsolvable), path tracking (curvature at colorless intersections).",[22,4772,4773],{},"Real tasks shine: distinguishes Chihuahuas from muffins via semantics + boxes; infers gummy bear heavier than cabinet from scale tilt; links Golden Gate box to Warriors NBA team; diagrams latte steps on espresso machine photo.",[17,4775,4777],{"id":4776},"outperforms-language-only-and-auxiliary-grounding-paradigms","Outperforms Language-Only and Auxiliary Grounding Paradigms",[22,4779,4780],{},"Text-only CoT (GPT-4V\u002FClaude3) fails on ambiguity. High-res cropping (InternVL) clarifies but can't cross-patch reference. Post-verification (GRIT\u002FDeepEyesV2) verifies linguistically. VGR aids but subordinates visuals.",[22,4782,4783],{},"DeepSeek makes primitives intrinsic: point-while-thinking drives reasoning, unlike Argus (2025 paper, arXiv:2505.23766) which explores architecture less deeply on data\u002Frewards.",{"title":105,"searchDepth":106,"depth":106,"links":4785},[4786,4787,4788,4789],{"id":4708,"depth":106,"text":4709},{"id":4738,"depth":106,"text":4739},{"id":4751,"depth":106,"text":4752},{"id":4776,"depth":106,"text":4777},[],{"content_references":4792,"triage":4807},[4793,4796,4799,4801,4803],{"type":4585,"title":4794,"url":4795,"context":4598},"Argus: Vision-Centric Reasoning with Grounded Chain-of-Thought","https:\u002F\u002Farxiv.org\u002Fabs\u002F2505.23766",{"type":4797,"title":4798,"context":4598},"dataset","COCO",{"type":4797,"title":4800,"context":4598},"Pixmo-Points",{"type":4797,"title":4802,"context":4598},"Pixmo-Count",{"type":4804,"title":4805,"author":4806,"context":4598},"tool","InternVL","Shanghai AI Laboratory",{"relevance":4600,"novelty":4601,"quality":4601,"actionability":106,"composite":4602,"reasoning":4808},"Category: AI & LLMs. The article discusses a novel approach to addressing the 'Reference Gap' in large multimodal models, which is relevant to AI product builders. However, while it presents interesting insights and performance metrics, it lacks specific actionable steps for implementation.","\u002Fsummaries\u002Fvisual-primitives-solve-lmm-reference-gap-summary","2026-05-05 07:50:52","2026-05-05 16:09:35",{"title":4698,"description":105},{"loc":4809},"0120dc1c893f4e5c","Data and Beyond","https:\u002F\u002Fmedium.com\u002Fdata-and-beyond\u002Fwhats-inside-the-mysterious-paper-deepseek-withdrew-at-lightning-speed-4351004f7c69?source=rss----b680b860beb1---4","summaries\u002Fvisual-primitives-solve-lmm-reference-gap-summary",[129,130,131,4819],"multimodal","DeepSeek's withdrawn paper introduces 'Thinking with Visual Primitives'—embedding bounding boxes and points into every reasoning step—to fix ambiguous referencing in multimodal models, achieving 77.2% on spatial benchmarks with 10x fewer tokens than rivals.",[4819],"aBKM4Y5tGRsHYr_PbxUz0bZ7hzLOTlZ2XFL4pOiU5d0",{"id":4824,"title":4825,"ai":4826,"body":4831,"categories":4879,"created_at":114,"date_modified":114,"description":105,"extension":115,"faq":114,"featured":116,"kicker_label":114,"meta":4880,"navigation":118,"path":4959,"published_at":4960,"question":114,"scraped_at":4961,"seo":4962,"sitemap":4963,"source_id":4964,"source_name":4965,"source_type":125,"source_url":4966,"stem":4967,"tags":4968,"thumbnail_url":114,"tldr":4970,"tweet":114,"unknown_tags":4971,"__hash__":4972},"summaries\u002Fsummaries\u002Ffinllm-phases-monoliths-to-multi-expert-traders-summary.md","FinLLM Phases: Monoliths to Multi-Expert Traders",{"provider":7,"model":8,"input_tokens":4827,"output_tokens":4828,"processing_time_ms":4829,"cost_usd":4830},8317,2879,23731,0.0030804,{"type":14,"value":4832,"toc":4873},[4833,4837,4840,4843,4847,4850,4853,4857,4860,4863,4867,4870],[17,4834,4836],{"id":4835},"finllm-evolution-delivers-domain-specific-agility-over-scale","FinLLM Evolution Delivers Domain-Specific Agility Over Scale",[22,4838,4839],{},"FinLLMs shift finance from discriminative classifiers (e.g., loan default prediction) to generative systems that synthesize markets, draft contracts, and execute trades. Phase 1 built proprietary monoliths like BloombergGPT (Wu et al., 2023), trained on 363B-token FinPile dataset (over 50% of training data), proving domain-specific pretraining beats generics but locks access behind trillion-dollar data moats. Phase 2 democratizes via FinGPT (Liu et al., 2023), using LoRA PEFT on base LLMs—adapt scholars with lightweight finance cheat sheets on laptops, validated by PIXIU benchmarks (Xie et al., 2023). Phase 3 assembles multimodal experts like Ploutos (Tong et al., 2024) and DISC-FinLLM (Chen et al., 2023), where specialists handle charts, audio, news; an LLM manager explains decisions in English, outperforming monolingual models in non-stationary markets.",[22,4841,4842],{},"Trade-off: Static monoliths decay fast amid volatile data; PEFT\u002Fmultimodal setups enable daily retraining without full recompute, cutting costs 100x while matching proprietary accuracy.",[17,4844,4846],{"id":4845},"diffusion-models-fix-data-bottlenecks-better-than-gans","Diffusion Models Fix Data Bottlenecks Better Than GANs",[22,4848,4849],{},"Finance data scarcity—paywalled, imbalanced, GDPR-locked—yields to synthetic generation. GANs like TimeGAN (Yoon et al., 2019) pit generator vs. discriminator but suffer mode collapse, ignoring black swans by overfitting simple patterns. Diffusion models (DDPMs) like FinDiff (Sattarov et al., 2023) and Diffolio (Cho et al., 2025) reverse-engineer noise into realistic time series, capturing volatility clustering, tail events, and correlations via thermodynamic principles—train by noising clean data then denoising, yielding stable what-if scenarios.",[22,4851,4852],{},"Impact: Ditch Monte Carlo for diffusion in 2026 stress tests; they model cross-sectional mess traditional sims miss, enabling privacy-safe backtests that behave like real markets without PII exposure.",[17,4854,4856],{"id":4855},"llm-rl-fusion-powers-hierarchical-trading-without-myopia","LLM-RL Fusion Powers Hierarchical Trading Without Myopia",[22,4858,4859],{},"RL bots optimize micro-trends but ignore macro (e.g., Fed hikes). Frameworks like Trading-R1 (Xiao et al., 2025) and FLAG-Trader (Xiong et al., 2025) layer LLMs as strategic Portfolio Managers—parse news, set theses, risk bounds—delegating tactics to RL Execution Traders minimizing slippage. Agentic RL adds autonomous API calls for live order books\u002Fbacktests; humans shift to orchestration (BCG, 2023).",[22,4861,4862],{},"Outcome: Brains (reasoning) + muscle (execution) harmony boosts Sharpe ratios in live volatility, per hierarchical abstraction (Darmanin & Vella, 2025).",[17,4864,4866],{"id":4865},"governance-trumps-scale-to-avert-flash-crashes","Governance Trumps Scale to Avert Flash Crashes",[22,4868,4869],{},"Black boxes violate EU AI Act\u002FESMA explainability (2024); Turing Trap replaces analysts sans causality. Worst: Model homogeneity triggers herding—identical LLMs hallucinate Fed signals, syncing sell-offs like GPS glitch gridlock (Xu et al., 2025). Metrics fail: BLEU irrelevant; use volatility-adjusted Sharpe in adversarial sandboxes.",[22,4871,4872],{},"Fixes: Mandate RAG for cited real-time data; embed liquidity constraints in loss functions; human-in-loop MRM registers (Bain 2023, PwC 2025) treat GenAI as 'synthetic personnel'. Multi-expert architectures ensure interpretability—regulators kill opaque giants, reward transparent ones for 5-year edge.",{"title":105,"searchDepth":106,"depth":106,"links":4874},[4875,4876,4877,4878],{"id":4835,"depth":106,"text":4836},{"id":4845,"depth":106,"text":4846},{"id":4855,"depth":106,"text":4856},{"id":4865,"depth":106,"text":4866},[],{"content_references":4881,"triage":4957},[4882,4886,4890,4894,4898,4902,4906,4910,4914,4918,4923,4927,4931,4935,4940,4944,4948,4952],{"type":4585,"title":4883,"author":4884,"url":4885,"context":4589},"DISC-FinLLM: A Chinese financial large language model based on multiple experts fine-tuning","Chen, W., Wang, Q., Long, Z., Zhang, X., Lu, Z., Li, B., … & Wei, Z.","https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.15205",{"type":4585,"title":4887,"author":4888,"url":4889,"context":4589},"Multimodal financial foundation models (MFFMs): Progress, prospects, and challenges","Liu, X.-Y., Cao, Y., & Deng, L.","https:\u002F\u002Farxiv.org\u002Fabs\u002F2506.01973",{"type":4585,"title":4891,"author":4892,"url":4893,"context":4589},"FinGPT: Democratizing Internet-scale data for financial large language models","Liu, X.-Y., Wang, G., Yang, H., & Zha, D.","https:\u002F\u002Farxiv.org\u002Fabs\u002F2307.10485",{"type":4585,"title":4895,"author":4896,"url":4897,"context":4589},"Ploutos: Towards interpretable stock movement prediction with financial large language model","Tong, H., Li, J., Wu, N., Gong, M., Zhang, D., & Zhang, Q.","https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.00782",{"type":4585,"title":4899,"author":4900,"url":4901,"context":4589},"BloombergGPT: A large language model for finance","Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., & Mann, G.","https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.17564",{"type":4585,"title":4903,"author":4904,"url":4905,"context":4589},"PIXIU: A large language model, instruction data and evaluation benchmark for finance","Xie, Q., Han, W., Zhang, X., Lai, Y., Peng, M., Lopez-Lira, A., & Huang, J.","https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.05443",{"type":4585,"title":4907,"author":4908,"url":4909,"context":4589},"Diffolio: A diffusion model for multivariate probabilistic financial time-series forecasting and portfolio construction","Cho, S.-Y., Kim, J.-Y., Ban, K., Koo, H. K., & Kim, H.-G.","https:\u002F\u002Farxiv.org\u002Fabs\u002F2511.07014",{"type":4585,"title":4911,"author":4912,"url":4913,"context":4589},"FinDiff: Diffusion models for financial tabular data generation","Sattarov, T., Schreyer, M., & Borth, D.","https:\u002F\u002Farxiv.org\u002Fabs\u002F2309.01472",{"type":4585,"title":4915,"author":4916,"url":4917,"context":4589},"Time-series generative adversarial networks","Yoon, J., Jarrett, D., & van der Schaar, M.","https:\u002F\u002Farxiv.org\u002Fabs\u002F1912.12440",{"type":4919,"title":4920,"author":4921,"url":4922,"context":4589},"report","Generative AI in the finance function of the future","Boston Consulting Group (BCG)","https:\u002F\u002Fwww.bcg.com",{"type":4585,"title":4924,"author":4925,"url":4926,"context":4589},"Language model guided reinforcement learning in quantitative trading","Darmanin, A., & Vella, V.","https:\u002F\u002Farxiv.org\u002Fabs\u002F2508.02366",{"type":4585,"title":4928,"author":4929,"url":4930,"context":4589},"Trading-R1: Financial trading with LLM reasoning via reinforcement learning","Xiao, Y., Sun, E., Chen, T., Wu, F., Luo, D., & Wang, W.","https:\u002F\u002Farxiv.org\u002Fabs\u002F2509.11420",{"type":4585,"title":4932,"author":4933,"url":4934,"context":4589},"FLAG-Trader: Fusion LLM-agent with gradient-based reinforcement learning for financial trading","Xiong, G., Deng, Z., Wang, K., Cao, Y., Li, H., Yu, Y., … & Xie, Q.","https:\u002F\u002Farxiv.org\u002Fabs\u002F2502.11433",{"type":4919,"title":4936,"author":4937,"publisher":4938,"url":4939,"context":4589},"Leveraging the promise of generative AI for financial risk management","Aziz, A.","SS&C Technologies","https:\u002F\u002Fwww.ssctech.com",{"type":4919,"title":4941,"author":4942,"url":4943,"context":4589},"Responsible by design: Five principles for generative AI in financial services","Bain & Company","https:\u002F\u002Fwww.bain.com",{"type":4919,"title":4945,"author":4946,"url":4947,"context":4589},"Leveraging large language models in finance: Pathways to responsible adoption","European Securities and Markets Authority (ESMA)","https:\u002F\u002Fwww.esma.europa.eu",{"type":4919,"title":4949,"author":4950,"url":4951,"context":4589},"Responsible AI in finance: 3 key actions to take now","PricewaterhouseCoopers (PwC)","https:\u002F\u002Fwww.pwc.com",{"type":4953,"title":4954,"author":4955,"url":4956,"context":4589},"event","Generative AI in Finance Workshop","Xu, R., Balestriero, R., He, J., Lee, Y., Wang, Z., Yu, Y., & Han, Y.","https:\u002F\u002Fneurips.cc\u002FConferences\u002F2025",{"relevance":4600,"novelty":4601,"quality":4601,"actionability":106,"composite":4602,"reasoning":4958},"Category: AI & LLMs. The article discusses the evolution of financial LLMs and their applications in trading, which aligns with the audience's interest in AI engineering and practical applications. While it presents novel insights into the use of diffusion models and their advantages over traditional methods, it lacks specific actionable steps for implementation.","\u002Fsummaries\u002Ffinllm-phases-monoliths-to-multi-expert-traders-summary","2026-05-03 16:04:29","2026-05-03 17:01:11",{"title":4825,"description":105},{"loc":4959},"43584fe9306eae40","Data Driven Investor","https:\u002F\u002Fmedium.datadriveninvestor.com\u002Ffinancial-llm-c191f0844ec5?source=rss----32881626c9c9---4","summaries\u002Ffinllm-phases-monoliths-to-multi-expert-traders-summary",[129,130,131,4969],"ai-automation","FinLLMs evolved from proprietary 50B-param giants like BloombergGPT, to open-source PEFT like FinGPT, to multimodal experts; fuse with diffusion synth data and RL for trading, but prioritize interpretability to dodge herding crashes.",[4969],"vNGHAtUK1_6V0-Om609EaVWFJ7YHcbd7pW9_-oyV4lg"]