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Decomposition Buys Integrity, Not Yield

Study of 600 production deep-research traces finds agent-tree decomposition loses findings at rate N^(1-δ); flat architectures maximize yield.

The paper models multi-agent decomposition as a tree where an agent holding b items retains each with probability r(b); with r(b)=1/b every tree delivers exactly one finding regardless of shape. Analysis of 600 production deep-research traces estimates delta=0.34 retention decay, and 1,012 annotated traces show one brief in sixteen goes off-target per tier, giving an alignment penalty of 0.536. Depth still cuts root context exposure from N to N^(1/k) and is cheaper at scale, with a hazard model over 743,819 production tool calls showing delegation is an opening move rather than a response to filling context.

arXiv cs.AI / cs.LG / cs.CL·Sep 15, 17:04 UTC · 1d agoAI research

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A Hybrid LSTM-XGBoost Framework for Multi-Horizon Stock Return Prediction Across Diversified Equity Portfolios

Hybrid LSTM-XGBoost model predicts multi-horizon returns for 14 US equities, cutting 30-day RMSE to about one-third of a standalone LSTM baseline.

The paper combines a two-layer LSTM (64 hidden units) processing 60-day windows of five market features with an XGBoost regressor over a 78-dimensional hybrid feature vector including 14 technical indicators. It is trained on pooled data for 14 US equities across six sectors using chronological splits and per-stock MinMaxScaling to prevent look-ahead bias, and evaluated at 30, 90, 252, and 365 trading-day horizons. The hybrid achieves test RMSE of 0.0949 at 30 days, roughly one-third of the standalone LSTM, while 97.6% directional accuracy at 365 days largely tracks the base rate of positive returns.

arXiv cs.AI / cs.LG / cs.CL·Sep 11, 17:51 UTC · 5d agoAI research

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Iceland-based Treble raises $18 million for its voice simulation platform

Iceland-based startup Treble raised an $18 million Series A extension to expand its acoustic simulation and synthetic data platform for voice AI companies.

Treble, founded in 2020 by acoustic engineers Finnur Pind and Jesper Pedersen, raised $18 million in a Series A extension led by Paladin Capital Group, bringing total funding above $40 million. The company builds physics-based acoustic simulation for synthetic speech data generation, voice AI model evaluation, and virtual prototyping of headphones, speakers, and smart glasses. Customers include Amazon and Logitech, and it partnered with Hugging Face earlier this year on a benchmark for speech recognition models under realistic conditions.

TechCrunch · AI·05:00 UTC · 6h agoAI industry

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Evaluating Time-Series Foundation Models and Multimodal Dietary Context for CGM Forecasting

Study finds zero-shot time-series foundation models underperform on CGM forecasting; fine-tuned Chronos-Bolt cuts RMSE up to 18.4% and dietary context adds signal.

The paper evaluates time-series foundation models for continuous glucose monitoring forecasting across eight public datasets covering Type 1 diabetes, Type 2 diabetes, and non-diabetes populations. Under a unified protocol, zero-shot foundation models did not consistently outperform baselines like Elastic Net and PatchTST, but lightweight fine-tuning did, with fine-tuned Chronos-Bolt reducing RMSE by 6.5%-18.4% in the T1D cohort and 8.6%-18.2% in the non-diabetes/T2D cohort. A residual-based fusion framework adding dietary context from CGMacros reduced overall RMSE by about 3% and postprandial RMSE by about 15% versus CGM-only baselines.

arXiv cs.AI / cs.LG / cs.CL·Sep 10, 17:43 UTC · 6d agoAI research

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Cadence: Error-Bounded Lossy Compression of Demand Time Series with a Time-Series Foundation Model

Cadence pairs Google's 330M-parameter TimesFM-3 foundation model with adaptive arithmetic coding, gaining 13-28% on 2026 demand series over classical predictors.

Cadence is an error-bounded lossy compressor for numeric time series combining the 330M-parameter Google TimesFM-3 foundation model with an adaptive arithmetic coder, guaranteeing a per-sample error bound. On 49 EIA-930 balancing-authority demand series from 2026 it gains 13.3% over the best of six classical predictors and 28.3% on 50 MTA ridership series, winning all 297 series-tolerance pairs with a 21.4% median gain. The paper also reports negative results, including that foundation models add negligible value for lossless coding and that PyTorch predictions are not bit-identical across batch sizes.

Hugging Face daily papers·Sep 4, 20:00 UTC · 12d agoAI research1

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A Princeton Researcher Proposes Recurrent Looped Transformer (RLT) that Carries Decoder State across Every Token, Fixing 96 Blocks per Token with Unbounded Temporal Depth

Princeton researcher Yifan Zhang proposes Recurrent Looped Transformer, carrying full decoder state across every token for unbounded temporal depth.

Yifan Zhang's technical report defines the Recurrent Looped Transformer (RLT), pairing a causal encoder with a recurrent decoder whose final output and layerwise sliding-window attention cache carry into every subsequent token with no prompt-response boundary reset. The reference configuration ties 48 encoder and 48 decoder layers, executing 96 logical blocks per token while the state path grows to 48t blocks after t tokens at fixed per-token compute. The report details RL replay contracts that rebuild all states under current parameters and exact prefix snapshots for multi-turn serving, but explicitly reports no measured efficiency, reasoning quality, or scaling results.

MarkTechPost·Sep 13, 17:02 UTC · 3d agoAI research1

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Reporting Practice Matters: The Impact of Reference Choice on Chest X-ray Report Evaluation

Study shows radiology reporting-style variations in reference reports can flip rankings of chest X-ray report generation models; releases MIMIC-CXR-Ext-ReRef dataset.

The paper quantifies how variations in radiologists' reporting practices distort evaluation of radiology report generation (RRG) models, introducing a radiologist-informed taxonomy and the ReRef method for rewriting reference reports while preserving clinical meaning. On MIMIC-CXR with RadCliQ-v1, condensing normal-findings discussion caused Libra to drop from first to second while CheXOne rose from third to first among nine models. The authors release MIMIC-CXR-Ext-ReRef, a radiologist-validated dataset of 120 original/alternative reference pairs, arguing metrics conflate clinical correctness with stylistic conformity.

arXiv cs.AI / cs.LG / cs.CL·Sep 16, 17:28 UTC · 18h agoAI research

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What must happen for AI’s trillion-dollar gamble to pay off

Hyperscalers need 2.7x productivity gains by 2030 to justify nearly $1.1 trillion in AI data center spending, or risk bankruptcy and capital misallocation.

Wharton finance professor Jessica Wachter estimates hyperscaler AI expenditure will reach nearly $1.1 trillion through 2027 and that a 2.7x productivity increase is needed to break even by 2030. AI revenues of roughly $150-200 billion this year fall far short of about $750 billion in annual spending, with total investment from Alphabet, Microsoft, Amazon, Meta, and Oracle potentially exceeding $5 trillion over four years. Alphabet reported its first free cash flow deficit (about $5.9 billion) since its 2004 IPO due to AI infrastructure costs. Researchers warn that failed demand could make the buildout the largest capital misallocation in history, with depreciating GPU chips risking stranded assets.

MIT Technology Review · AI·Sep 15, 10:00 UTC · 2d agoAI industry

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TART: A Modular Tool for Technique-Aware Audio-to-Tablature Guitar Transcription

TART pipeline transcribes guitar audio to tablature with fingering and technique labels, outperforming prior baselines by up to 8.5 Tab F1 points.

TART is a modular four-stage pipeline for audio-to-tablature guitar transcription, combining an audio-to-MIDI transcription model, an expressive technique classifier, an audio-conditioned T5 encoder-decoder for string-fret assignment, and an automated tablature generator. In zero-shot evaluation on GuitarSet, EGDB, and two noisy augmented benchmarks, it averages 81.35% audio-to-MIDI F50 (+6.67 over the best baseline), 71.8% string-fret Tab F1 (+8.5), and 54.08% end-to-end Tab F1. The authors state it is the first framework to generate tablature with both fingering and expressive technique annotations directly from guitar audio.

arXiv cs.AI / cs.LG / cs.CL·Sep 10, 17:55 UTC · 6d agoAI research2

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Function Name Is All You Need to Detect Blockchain Application Attacks

TxLucent detects blockchain dApp attacks from transaction function-name sequences using a transformer, achieving 1.56% false negatives without source code.

Researchers propose TxLucent, which maps transaction call traces to function name sequences and uses a transformer to detect blockchain application attacks without source code or handcrafted rules. Evaluated on 424 real-world incidents with 14,611 attack transactions, it achieves a 1.56% false negative rate and an estimated 0.0017% false positive rate across over 500 million Ethereum transactions. Average analysis time of 24.90 milliseconds supports real-time detection on popular blockchains.

arXiv cs.CR·Sep 11, 00:43 UTC · 6d agoResearch1

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Learning Length-Extrapolatable Recurrent Models

Researchers propose Credit Stabilization through Time, a training method letting recurrent models extrapolate up to 128x their training length.

The paper argues that length extrapolation failure in BPTT-trained recurrent models is better explained through state credit, the signal through which future losses reach earlier recurrent states. It introduces Credit Stabilization through Time (CST), which locally rescales the state-credit signal during backpropagation without rotating the corrected component or changing forward computation. Controlled experiments show improved performance beyond the training horizon, with gains at up to 128x the training length.

arXiv cs.AI / cs.LG / cs.CL·Sep 8, 17:59 UTC · 8d agoAI research1

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Nvidia dismisses "circular financing", says every $1 it invests brings back $100

Nvidia rejects 'circular financing' criticism, claiming every $1 it invests yields $100 in returns amid a falling stock price.

Nvidia publicly dismissed concerns that its AI ecosystem investments amount to 'circular financing', asserting that each $1 it invests generates roughly $100 in value. The statement comes as Nvidia's share price continues to decline. The story drew moderate discussion on Hacker News with 34 points and 25 comments.

Hacker News · security·Sep 13, 10:29 UTC · 4d agoAI industryHN 34↑ · 25 comments

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How Model Growth, Recursion, and Boundary Operators Influence Scaling Exponents

Researchers show model growth via looped transformers improves scaling exponents; a 7.4B architecture matches GPT-3 13B with roughly 20x less compute.

The paper shows that architectural interventions, contrary to conventional wisdom, can modify pre-training scaling exponents and yield exponential performance gains with compute. Looped transformers with increasing loop counts provide a model growth mechanism; a 7.4B model-growth architecture matches GPT-3 13B on CORE with roughly 20x less compute, with efficiency gains that increase with scale. A boundary operator that normalizes and injects an earlier block also improves compute efficiency, and in data-constrained multi-epoch settings increasing loops with scale is compute-optimal.

arXiv cs.AI / cs.LG / cs.CL·Sep 16, 17:36 UTC · 18h agoAI research

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RTK reports token savings, but our cost benchmarks disagree

Quesma's $1,500 benchmark found RTK cuts reported token output but changes Claude Code and DeepSeek coding costs by only about 5% on Terminal-Bench 2.1.

Quesma benchmarked RTK (Rust Token Killer), a popular tool with 79k GitHub stars that filters terminal output for AI coding agents, whose README claims up to 90% output reduction. Across 1,740 Terminal-Bench 2.1 attempts running Claude Code with Fable 5.0 and OpenCode with DeepSeek V4 Pro 0813, total costs moved only -5% for Fable and +5% for DeepSeek, with pass rates dropping 1-2%. RTK's own rtk gain metric reported 349.2 million tokens saved (an 89% reduction) across 445 DeepSeek attempts, but this did not correlate with actual cost savings, and cached terminal-output reads cost as little as 1/10 to 1/30 of regular input tokens. A bug in rtk find 0.45.0 caused one agent to loop with 339 consecutive errors, costing roughly 9x the baseline attempt, though the task still passed.

Hacker News · security·Sep 11, 11:15 UTC · 6d agoAI tools & infraHN 28↑ · 10 comments1

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Learning Sparse Decision Trees via Transformer Variational Auto-Encoders

TREVIS uses a Tree Transformer VAE latent space to learn decision trees matching near-optimal predictive performance while improving structural sparsity.

TREVIS learns decision trees optimized for complex objectives by exploring the latent space of a Tree Transformer Variational Auto-Encoder (TTVAE). Mapping trees to continuous latent representations replaces the discrete search space with a continuous one, enabling gradient-based optimization through a differentiable surrogate model. Experiments show TREVIS matches the predictive performance of near-optimal algorithms while improving structural sparsity, targeting high-stakes contexts needing transparent decision logic.

Hugging Face daily papers·Aug 31, 20:00 UTC · 16d agoAI research1

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T1: Terminal Agent Reinforcement Learning for Long-Horizon Tasks

T1, a 122B MoE terminal agent trained with reinforcement learning, reaches 64.0% on Terminal-Bench 2.1, surpassing GPT-5.4 and GLM-5.1 on long-horizon tasks.

T1 is a 122B mixture-of-experts model trained with reinforcement learning to operate a real shell in a cloud sandbox for up to 300+ tool-call turns per task, rewarded by executing each task's own verifier. The recipe combines aggressive warm starts, dense process rewards, TITO construction, and rollout routing replay, cutting the training-to-inference log-probability difference from 0.021 to 0.013 with zero token drift. Training used an out-of-distribution corpus disjoint from Terminal-Bench 2.1. Post-training raised the base model from 43.8% to 64.0% resolved on Terminal-Bench 2.1 and 27.9% on Long-Horizon Terminal Bench.

Hugging Face daily papers·Sep 9, 20:00 UTC · 7d agoAI research1

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Heterogeneous Cross-Chain Transaction Tracing for Solana Bridges via Candidate-Set Selective Decision

SolTracer traces cross-chain transactions onto Solana bridges, improving open-world association F1 by 20.16% over the strongest baseline for illicit-fund tracing.

The paper formalizes four Solana-bound cross-chain transaction modes and proposes SolTracer, which maps heterogeneous execution semantics into a unified event space and uses candidate-set selective decision-making with abstention when valid targets are absent. In the challenging open-world setting with a 50% TA ratio, SolTracer improves F1 by 20.16% over the strongest baseline. An empirical study of real-world transfers examines count-value divergence across bridge mechanisms, cross-asset shifts, and decoupling between on-chain settlement and explorer visibility.

arXiv cs.CR·Sep 10, 11:43 UTC · 7d agoResearch

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Technical Manual for a Toolkit for Measuring Contextual Individuation in Transformer Language Models

An open methodology toolkit measures whether transformer language models contextualize fixed word forms across domains using bridge forms and layer-wise silhouette analysis.

The manual documents an open toolkit built around 'bridge forms' - identical written words recurring across two or more subject domains with a different sense in each - to test whether transformer language models individuate word occurrences by context beyond the embedding layer. It covers declarative specification of bridge forms, Wikipedia corpus acquisition, occurrence localization, layer-wise representation extraction, domain-pairwise silhouette measurement, and visualization, justifying each choice against failure modes such as sense contamination and subword-tokenization misalignment. It is a methodological and implementation reference and reports no empirical results.

arXiv cs.AI / cs.LG / cs.CL·Sep 4, 16:38 UTC · 12d agoAI research

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Transfer Learning for Evolving Domains

TrED formalizes transfer learning for domains whose data availability evolves over time, arguing classical settings are regimes along one trajectory, and remains unsolved.

The paper introduces Transfer Learning for Evolving Domains (TrED), formalizing transfer learning as a trajectory problem where target data and labels are progressively collected. TrED is specified by a data availability process fixed by the environment, a freely chosen learning protocol, and an evaluation criterion scoring the whole trajectory of models. Classical settings like domain generalization, domain adaptation, and multi-domain learning are recovered as regimes within this framework. The authors survey the literature and find most methods are tailored to a single regime, leaving TrED a well-posed open problem.

arXiv cs.AI / cs.LG / cs.CL·Sep 11, 16:37 UTC · 5d agoAI research

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What LLM Trading Agents Actually Do in Production: A Six-Month, Population-Scale Record from Two Fleets

Six-month record of 7.5M LLM trading agent invocations shows volatility-blind sizing, minimal upside capture, and no directional edge across two fleets.

The study records autonomous LLM trading agents in production across DX Terminal Pro (3,505 user-funded vaults trading real ETH in Base memecoin markets) and the DXAP fleet (500-599 agents on Hyperliquid perpetuals), spanning roughly six months, 7.5M single-model invocations and about 300K onchain actions. A risk slider explains leverage (+0.425 per level), median leverage is 5.0x in every volatility sextile, and one posture-slider cell holds 62% of liquidations. Agents capture little upside: 43.2% of positions saw +300 bps favorable excursion within 24h yet 49.3% of those closed negative, while the DXAP fleet trails a matched retail benchmark (41% vs 50% roundtrip win rate). A paired-replay league of frontier models finds decision quality statistically indistinguishable at this horizon.

Hugging Face daily papers·Sep 3, 20:00 UTC · 13d agoAI research

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EvolveTrade: Experience-Driven Policy Refinement for Self-Evolving LLM Trading Agents

EvolveTrade lets LLM trading agents self-refine their tool-use policy from realized portfolio feedback, improving Sharpe ratios.

EvolveTrade treats a tool-using trading agent's system prompt as a text-parameterized policy that a Policy Agent revises after each update interval using accumulated decision traces and realized portfolio feedback, keeping the backbone LLM fixed. Experiments across multiple market regimes and two LLM backbones show improved Sharpe Ratio and Cumulative Return over fixed-policy baselines in most settings. Behavioral analyses show evolved policies increase code-mediated analysis and activate regime-relevant computations, with case-level attributions linking policy changes to returns.

Hugging Face daily papers·Sep 14, 20:00 UTC · 2d agoAI research

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OpenRouter's staggering token chart is the AI bubble debate in a single image

OpenRouter weekly token consumption surged 25,000% since January 2025, but reasoning-model 'thinking' tokens inflate the metric beyond real adoption.

OpenRouter data shows weekly token consumption grew from 0.5 trillion to 126.2 trillion tokens since January 2025, a rise of over 25,000%. The Decoder argues the surge reflects inflated token metrics from reasoning models' 'thinking' tokens and unoptimized agentic workloads rather than proportional growth in usage or business value. OpenAI's GPT 5.6 Luna dominates token consumption while Astra leads revenue, and Chinese models Kimi, GLM, and DeepSeek saw monthly spending grow tenfold in 2026 from a small base.

The Decoder·10:24 UTC · 1h agoAI industry

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After warning AI is too dangerous, Bill Gates bets a billion on its upside

Gates Foundation pledges at least $1 billion over two years to widen AI access in health, education and agriculture, warning of a rich-poor divide.

The Gates Foundation's 2026 Goalkeepers report outlines spending of at least $1 billion over two years on AI access in health, education and farming. Gates notes over 90% of early LLM training data was English, with speech recognition error rates below 6% in English but above 60% in Yoruba. Cited projects include Penda Health clinics in Kenya (16-point diagnostic accuracy gain), Gemini Guided Learning in Sierra Leone (1.7 years of learning gains in eight weeks), and India's MahaVISTAAR reaching 740,000+ farmers at under 18 cents per person.

The Decoder·Sep 15, 14:26 UTC · 1d agoAI industry1

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Molecular Déjà Vu: Digit-Level Retrieval of Published Values in Frontier Language Models

Audit of 22 frontier models finds widespread verbatim retrieval of published molecular property values, with higher reasoning increasing recall of memorized numbers.

An arXiv audit tests 22 frontier LLMs across 12 molecular regression benchmarks for verbatim retrieval of published values. More than 50% of the LLMs show verbatim retrieval on five datasets, and identical experiments are flagged 89% more often at a high reasoning level than at the lowest one. Suppressing retrieval moves model prediction errors closer together in relative terms, suggesting predictive capability is not determined solely by memorized values.

arXiv cs.AI / cs.LG / cs.CL·Sep 4, 17:32 UTC · 12d agoAI research1

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Kaininja: Extending Native 3D Generators to the Part Level

KaiNinja extends TRELLIS.2 native 3D generation to part-level assets via a dual-volume O-Voxel representation, cutting whole-object Chamfer distance by 40%.

KaiNinja extends the TRELLIS.2 native 3D generator to produce part-level assets instead of one fused mesh, enabling downstream editing, rigging, and simulation. A dual-volume form of the O-Voxel representation solves the problem that a single volume cannot represent interfaces where two parts touch. The model needs no segmentation network, is partly trained on LLM-agent-authored part data, lowers whole-object Chamfer distance by 40%, and raises strict part F-score by 16% versus other part-generation pipelines.

Hugging Face daily papers·Sep 13, 20:00 UTC · 3d agoAI research

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Meme Coin Factories: Uncovering Large-Scale Manipulations on pump.fun

Large-scale pump.fun study of 15 million meme coins identifies five manipulation classes including wash trading and a Market-Manipulation-as-a-Service ecosystem.

Researchers analyzed all 15 million coins launched on pump.fun over the last two years plus large random samples of transaction data, identifying five manipulation classes: wash trading, creator address obfuscation, coordinated sells, copycat coins, and social media manipulation. Strategic actors bypass the platform interface and implement strategies in a highly automated, low-latency way by interacting directly with the blockchain. The study also uncovers Market-Manipulation-as-a-Service (MMaaS) third-party tools that let non-technical users run these manipulations, and proposes mitigations for traders, pump.fun, and regulators.

arXiv cs.CR·Sep 9, 14:35 UTC · 7d agoResearch

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Generative Marketing Mix Modeling: A Causal Inference Framework Linking GEO and GEM to Business Impact

Paper proposes Generative Marketing Mix Modeling to causally estimate Generative Engine Optimization and Marketing effects on business outcomes.

The authors develop GMMM, a causal inference framework for measuring how often users see and notice a firm's name in generated answers, which standard marketing data ignore. For GEO it combines repeated generated answers with question counts, shares of generative-system usage and notice probabilities; for GEM it uses sponsored placement records with notice probabilities. The framework compares expected business responses under alternative treatment sequences, establishes identification conditions, and is evaluated on simulated product-recommendation answers in English and Japanese.

arXiv cs.AI / cs.LG / cs.CL·Sep 10, 17:57 UTC · 6d agoAI research

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You've Got a BUD in Me: Authenticated Reads from Per-Block Write Logs

Researchers propose BUD, per-block write-log digests enabling blockchain validators to serve historical membership and exclusion proofs far cheaper than state-wide tries.

The paper introduces Block Update Digests (BUD), which authenticate each block's write log with predecessor pointers, plus a SuperBUD and exponential hierarchy to turn long unchanged intervals into short proofs. Soundness against adversarial provers and up to f Byzantine validators is proven under archive, attestation, and committee evidence assumptions. Benchmarks show a 50x state-size increase raises the base-BUD path only 1.24x versus 3.1x for in-memory and 69.5x for disk-backed Merkle Patricia tries, with read payloads below 800 bytes and p99 warm verification at 146 microseconds.

arXiv cs.CR·Sep 10, 08:46 UTC · 7d agoResearch

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Operational Roles of QRNG-Derived Quantum Entropy in Bitcoin Proof-of-Work Architectures

arXiv study finds quantum-random entropy adds no Bitcoin PoW success advantage but helps assurance in fault and provenance scenarios.

The paper shows replacing classical entropy with QRNG output does not change honest Bitcoin proof-of-work success probability when candidate headers remain distinct. It introduces a reproducible benchmark measuring an entropy-efficiency factor and a reboot-diversity index, finding QRNG value only in assurance-oriented scenarios involving correlated restart faults, namespace reuse, and entropy provenance. Validation is simulation-based, with hardware-in-the-loop testing identified as future work.

arXiv cs.CR·Sep 4, 12:47 UTC · 12d agoResearch

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Show HN: Sunk Cost – How long until a local LLM rig pays for itself?

Show HN tool 'Sunk Cost' calculates when a local LLM rig breaks even versus falling API prices, factoring electricity cost and inference speed.

A Hacker News Show HN project called Sunk Cost models the payback period of buying local LLM hardware instead of paying API prices. Users can adjust assumptions like electricity cost ($/kWh) and API speed (tokens/second), and the model assumes API prices keep falling. Where local speed is unmeasured, it is estimated from memory bandwidth divided by bytes read per token, and labelled as an estimate.

Hacker News · security·Sep 15, 01:37 UTC · 2d agoAI tools & infraHN 30↑ · 31 comments

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