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Benchmarking LLM Agents for Earnings Forecasting","summary":"Quarterly earnings forecasting requires an agent to connect financial evidence to the income-statement line items that produce a company's reported earnings. We introduce EPSBench, a benchmark for studying this ability across 149 firm-quarters from 86 US companies. Agents read a fixed collection of filings, earnings-call transcripts, and news, then estimate company-specific income-statement drivers with cited evidence. A shared accounting calculator converts these estimates into GAAP diluted earnings per share (EPS), providing a common basis for comparing forecast assumptions. We evaluate six models under a common harness, assessing point forecast error relative to date-matched analyst consensus and checking whether their prediction intervals achieve the stated coverage. Three models show no detectable paired difference in point forecast error from consensus, and three have higher error. Across all six models, nominal 80% intervals contain reported EPS in only 37--70% of runs that return an interval, and most misses fall above the upper bound because the forecasts run low. The driver records trace most of the error gap between models to operating-income estimates.","upvotes":1,"at":"2026-10-28T01:24:11.000Z"},{"id":"2610.38681","title":"Weaken to Strengthen: Weakening-Induced On-Policy Self-Distillation for Large Reasoning Models","summary":"Self-distillation offers a way to improve reasoning models when a suitable external teacher is unavailable. However, common approaches rely on privileged information to construct a stronger self-teacher, limiting their use when only unlabeled prompts are available. In this paper, we introduce Weakening-Induced On-Policy Self-Distillation (WISD), which instead constructs a weaker counterpart of the original model and uses the contrast between the two as a training signal without requiring privileged information. We explore two ways to construct the weaker model: low-bit quantization and magnitude pruning. We evaluate WISD across diverse architectures and scales on mathematical and coding reasoning tasks. Across these benchmarks, quantization and pruning improve average accuracy over the original models by 4.0 and 5.5 percentage points, respectively, achieving state-of-the-art performance even without privileged information. Notably, pruning raises Qwen3.5-4B's accuracy from 57.7% to 70.5% on HMMT 2025 and from 39.1% to 56.7% on LiveCodeBench v6. Quantization raises Qwen3.6-35B-A3B's accuracy from 68.5% to 74.6% and from 61.8% to 69.5% on the same benchmarks, respectively. These results establish WISD as a novel and effective self-distillation framework for large reasoning models.","upvotes":1,"at":"2026-10-27T20:00:00.000Z"},{"id":"2610.39293","title":"Unlocking Any-Order Generation in Pretrained Autoregressive Image Models","summary":"Autoregressive (AR) image generators are powerful in principle but inflexible in practice: training with a fixed raster-scan order hardcodes a single factorization of the image distribution into the model, leaving it unable to inpaint, outpaint, or edit at inference time. Existing solutions, including the modern discrete-diffusion family, train from scratch on a mixture of token orderings, paying roughly 3x the compute of standard raster-scan training; discrete diffusion further sacrifices compatibility with the AR inference ecosystem (KV caching, vLLM) by construction. We argue that this cost is unnecessary. A pretrained raster-scan AR model has already learned the joint distribution over image tokens; what it lacks is not knowledge but the ability to express that knowledge through non-sequential conditional pathways. We propose a recipe for adapting pretrained raster-scan models into any-order generators using two components: future-aware positional embeddings that resolve a target-position identifiability problem at negligible overhead, and an ordering curriculum that traces a stable optimization path from the pretrained solution to a good any-order basin. Applied to four base models across two architectures and two scales (LlamaGen-L/XL, RAR-L/XL), the recipe substantially outperforms MaskGIT on ImageNet inpainting (FID 6.21 vs. 10.44) and outpainting, is competitive with MAR-L, trades a 1.2-1.9 increase in class-conditional FID for this flexibility, and costs 13-33% additional compute beyond pretraining, while leaving the standard AR inference stack untouched. Inpainting, outpainting, and editing, our results suggest, need not be added to autoregressive image models. They can be unlocked from the ones that exist. Code is available at https://github.com/Ordered-AR/unlockedadaptation.","upvotes":2,"at":"2026-10-28T20:00:00.000Z"},{"id":"2610.34298","title":"Matched Capabilities, Different Behaviors: A Controlled Study of MCP and CLI Interfaces for Language Agents","summary":"Language agents increasingly act through external tools and interactive environments, making the interface between the model and its executable capabilities part of the decision process. Model Context Protocol (MCP) and command-line interfaces (CLI) can expose the same semantic operations through markedly different model-facing representations. We study whether these representational differences change agent behavior when executable capabilities are matched. We introduce Matched Interface Comparison and Analysis (MICA), which renders MCP and CLI from a shared semantic action contract and routes decoded actions through the same runtime. Across six models and three interactive environments, matched interfaces produce substantial differences in task performance and interaction cost. These effects extend beyond surface validity to semantic action selection, failure pathways, and recovery. Matched-state probes localize sensitivity to capability presentation, realization, and feedback. Paired natural trajectories reveal structured divergence in 94.3% of 2,004 episodes and show that early differences propagate through subsequent interaction. This analysis identifies interface realization as a direct intervention boundary. We therefore introduce Decoupled Action Realization and Targeted Repair (DART), which preserves validated semantic commitments while repairing interface-specific realization. DART improves benchmark score in all six evaluated settings by up to 9.0 points and reduces MCP token cost in all three environments by up to 27.1k tokens per task. Together, these results establish interface representation as part of the closed-loop decision environment shaping agent behavior.","upvotes":3,"at":"2026-10-24T20:00:00.000Z"},{"id":"2609.05881","title":"Broken on Arrival: Non-Functional Quantized LLM Artifacts in Public Model Registries and How to Catch Them","summary":"Developers download quantized large language models (LLMs) from public registries to run them locally. None of the registries we examined documented functional testing of their files. We tested all 305 eligible code-capable GGUF tags (the llama.cpp format) across 15 model lines in the official Ollama library, plus 22 popular Hugging Face community files. A 15-task code screen identified suspects, which we retested on all 164 HumanEval+ tasks. Seven official tags (2.3%) solved at most six of them. All were low-bit quantizations of models with at most four billion parameters. We rebuilt five suspect files from the original weights. All five registry files matched our uncalibrated rebuilds tensor for tensor, so none was corrupted. Exact reproducibility did not imply function. With the weights, toolchain and tensor mix held fixed, adding an importance matrix raised three Qwen2.5-Coder-3B builds from zero to 76-113 solved tasks. The same uncalibrated recipe left the smaller Qwen2.5-Coder-1.5B functional, showing that the recipe alone does not predict failure. None of 66 sampled tags that passed the screen was non-functional. We release the audit data and the quantcheck tool and recommend screening low-bit tags before publication, then calibrating or withholding failures.","upvotes":3,"at":"2026-10-24T20:00:00.000Z"},{"id":"2610.40564","title":"Training Foundation Models with a Single Algorithm","summary":"Foundation models are typically trained in stages: pretraining, midtraining, supervised fine-tuning (SFT), and reinforcement learning (RL), with changes in both data and learning algorithms. We show that this algorithmic separation is not fundamental. By deriving suitable RL objectives as maximum-likelihood fitting to model-generated target distributions, we place all of these stages under a single likelihood-based training algorithm, unified training. This perspective turns the foundation-model training lifecycle into a problem of choosing which data to train on, in what proportions, and when, with conventional staged training as a special case. We instantiate this framework in a unified training system that mixes static data and RL generations within a single trainer. Across controlled and real-world experiments, unified training improves the accuracy-compute tradeoff and task coverage while preserving broader capabilities, and provides a stronger starting point for continual learning. This work lays the groundwork for a unified science of foundation-model training, connecting mathematical principles, training infrastructure, and the development of model capabilities.","upvotes":3,"at":"2026-10-28T20:00:00.000Z"},{"id":"2610.23656","title":"ABF-Bench: A Verifiable Benchmark for Agentic Blockchain Forensics","summary":"Blockchain forensics is adversarial and open-ended: money launderers split, swap, and bridge funds, cut their trail at mixers or custodial services, and change tactics from one incident to the next. Investigators must keep adapting their tracing in response. Yet the immutability of public ledgers preserves every recorded movement in public view, making much of the investigation independently verifiable. We introduce ABF-Bench, an interactive benchmark that makes evidence-backed fund tracing a verifiable agent task, in which every claimed movement must cite ledger records the agent retrieved. A case-to-task pipeline builds well-posed tasks across ledgers: conservative rules halt tracing wherever the records leave a continuation unresolved, and each task's reference graph is the answer those rules determine. Built from real laundering activity, the tasks combine multi-hop transfers, DeFi exchanges, cross-chain round trips, custodial and mixer deposits, and address poisoning. We also propose the ABF agent harness, built around a working memory, and evaluate it alongside general-purpose agents and fixed-order tracers. The harness reaches 75% edge recall, against 47% for Codex with the same model and 10% for Claude Code, which loses recall to overconfidence: it cites records it left unretrieved. Compared with fixed-order tracers, the harness acquires evidence faster than the best of them early in the budget and recognizes undisclosed custodial services that they walk past (89% vs. 0%), with 98% fidelity. However, agents tend to stop early and lose recall on larger tasks, which holds their final recall below the best tracer. Beyond exposing these gaps, ABF-Bench opens blockchain forensics to LLM agents as a verifiable domain, where the verdicts that score agents also offer potential training signals.","upvotes":3,"at":"2026-10-15T20:00:00.000Z"},{"id":"2610.39302","title":"Proving and Improving the Platonic Representation Hypothesis with JEPAs","summary":"Whether models trained on different modalities converge to the same representation of the world is central to AI. The Platonic Representation Hypothesis (PRH) conjectures that they do as they scale, but without a proof it offers neither a guarantee nor a recipe. We turn the hypothesis into a theorem. First, we prove a Platonic Representation Theorem: Joint-Embedding Predictive Architectures (JEPAs) trained separately on any number of modalities learn the same representation up to one rotation whenever their positive pairs are two observations of one scene--good views--and their misalignment is bounded otherwise. Second, we show that next-token prediction is a JEPA with bad views, parts of a single document, which explains why language models align with vision only through scale. Third, we validate the theory from 145k captions to the texts of 940M web images, and uncover a scaling law of alignment that predicts unseen runs within seed noise. Good views need 42 times fewer training samples than next-token prediction on the same corpus to reach the same alignment, and a 109M-parameter text JEPA aligns with DINOv2 and CLIP's image encoders better than every language model we evaluated, up to 72B parameters, and than CLIP's text encoder: next-token prediction is a remarkably inefficient route to platonic representations. As a showcase, one rotation turns separately trained encoders into an image captioner (CIDEr .838 on COCO). Even that rotation needs no pair: on the latest pair-free benchmark, our text JEPA beats every zero-shot accuracy reported with language models up to 8B parameters, e.g., 64\\% against at most 56\\% top-5 on ImageNet-100. Positive views, and not scale alone, are thus the lever to build platonic representations.","upvotes":54,"at":"2026-10-28T20:00:00.000Z"},{"id":"2610.38174","title":"Scaling Lean Theorem Proving for Real-World Code Verification","summary":"We present EasyProver, a Lean proving system built from recursive agents with isolated contexts, asynchronous search, parallel tactic probing, and an elastic elaboration service. Using an off-the-shelf frontier model with no additional training, it proves 669/672 PutnamBench problems and all six IMO 2026 formalizations and resolves 597/600 NTP4VC verification conditions and all 189 VERINA tasks by proof or refutation. Because these benchmarks are close to saturation, we also introduce Aegis, which converts competitive programming problems into much harder verification tasks over real implementations. The pipeline writes safe Rust solutions, translates them into Lean with Aeneas, generates specifications, and labels each frozen statement with a kernel-checked proof or refutation. It produces 2,045 differentially validated Rust-Lean program pairs and 11,533 candidate specifications. Unlike hand-written benchmarks, these tasks carry implementation-level obligations from fixed-width arithmetic, explicit failure, generated loops, casts, and functionalized mutation. On a stratified sample of 2,000 statements, EasyProver proves 1,054 (52.7%) within a 30-minute budget per statement. The same tasks can also be used to benchmark other provers and as rewards for reinforcement learning.","upvotes":2,"at":"2026-10-27T20:00:00.000Z"},{"id":"2610.39423","title":"FDSML: Formula-Driven Supervised Metric Learning with Parameter-Aware Triplet Loss","summary":"Formula-Driven Supervised Learning (FDSL) enables vision pre-training without real images, but existing methods cast pre-training as classification on discretized labels, discarding continuity in the generative process. We propose Formula-Driven Supervised Metric Learning (FDSML), which derives relational supervision from image-generation parameters beyond discrete class labels. FDSML combines FineFractalDB, our refined fractal dataset with higher intra-class variation and more stable structures, with a Parameter-Aware Triplet (PA-Triplet) Loss that assigns positives and negatives according to distances in the underlying Iterated Function System parameter space. This objective trains embeddings to respect parameter-derived relative similarity orderings. Experiments show that FDSML improves transferability to image retrieval while remaining competitive on downstream classification and improving robustness under several corruption settings. On ResNet-50, Recall@1 improves from 65.6% to 74.0% on Stanford Online Products and from 56.2% to 78.4% on Stanford Cars over FDSL pre-training. These results suggest that parameter-derived relational supervision provides a stronger synthetic pre-training signal than label-based classification for transfer beyond recognition.","upvotes":1,"at":"2026-10-28T20:00:00.000Z"},{"id":"2610.25276","title":"Holo4D: Holistic 4D Reconstruction as Geometric Control for Video Diffusion","summary":"Synthesizing a monocular video from a new camera trajectory requires preserving the observed scene geometry and motion while completing regions that become newly visible. Feed-forward 4D reconstructors recover the structure of the observed scene, whereas video diffusion models provide a generative prior for completing unseen content. We introduce Holo4D, a video reshooting framework that uses reconstructed 3D trajectories as a holistic source-view 4D scaffold. We project these trajectories into the requested views and encode their geometry and source appearance to guide a pretrained video diffusion model. Together with the source video and target cameras, the scaffold grounds generation in reconstructed observations while leaving unsupported regions to the generative prior. Experiments demonstrate the value of trajectory-based conditioning for camera control and novel-view synthesis. The framework accommodates different 4D reconstructors without retraining the video generator, allowing it to benefit from future advances in source reconstruction. More details can be found in our project page.","upvotes":2,"at":"2026-10-17T20:00:00.000Z"},{"id":"2610.39301","title":"Youtu-Parsing-Omni: One Encoder, One Schema, All Modalities","summary":"We present Youtu-Parsing-Omni, an omni-modal parser that converts documents, images, audio, and video into structured records grounded in space and time, providing a perception layer for agents that read, locate, and cite evidence across modalities. Its design rests on one encoder for every modality and one schema for every task. Youtu-Omni-Encoder, initialized from a pre-trained language model to strengthen text reading, encodes all modalities with shared weights in a unified space-time coordinate system, and a few fusion layers let video frames attend to concurrent audio, bringing cross-modal fusion and temporal alignment into perception itself. OmniSchema unifies the outputs of all ten parsing tasks, allowing a single self-evolving post-training procedure, OmniSchema-Aware On-Policy Distillation (OSAD), to provide a shared token-role-based objective across tasks. In each round, the model is trained on its own rollouts against token-level targets from a reference-conditioned copy of itself, which is then refreshed with the trained weights, so that the parser and its supervision co-evolve. With a single 5 B-parameter checkpoint, Youtu-Parsing-Omni achieves state-of-the-art performance on OmniDocBench v1.6 (96.96 Overall) and leads all open-weight models on OmniParsingBench (75.08 Avg.), with its advantage concentrated on audio and video. It also achieves the best overall chart-parsing score (95.02) and the highest exact chemical-fingerprint agreement among compared systems, and matches a dedicated optical-music model in character-level transcription of typeset sheet music while staying far ahead of general-purpose systems. Code and model weights are publicly available.","upvotes":56,"at":"2026-10-28T20:00:00.000Z"},{"id":"2610.39287","title":"OpenFlyV2: An onboard vision-language-action model for aerial perception and navigation","summary":"Human cognition flexibly draws on a shared perceptual foundation to understand environments, attend to referenced entities, reason about spatial relationships, and act. Endowing aerial robots with an analogous functional organization remains challenging, as existing systems are often specialized for individual capabilities, dependent on offboard computation, or unable to sustain closed-loop operation on resource-constrained platforms. We present OpenFlyV2, a unified onboard vision-language-action architecture that uses a shared vision-language representation to support semantic understanding, language-grounded perception, spatial reasoning, and continuous aerial control within a single model. A compact action expert converts the shared multimodal context into short-horizon flight waypoints, while motion-aware temporal memory and action priors reduce the computational cost of closed-loop execution. We construct , a simulation-based dataset comprising approximately 70,000 navigation trajectories and 60,000 vision-language instruction--response pairs. OpenFlyV2 generates action chunks, each comprising 12 waypoints, entirely onboard at 10 Hz using an NVIDIA Jetson Orin NX. It transfers zero-shot from simulation to physical quadrotor flight, achieving a 48\\% navigation success rate across four real-world environments without real-world aerial training data. Extensive results demonstrate that broad vision-language competence and continuous closed-loop flight can coexist within a single model on a resource-constrained aerial platform. Code will be made publicly available at https://github.com/Ideal-ljl/AeroBrain.","upvotes":4,"at":"2026-10-28T20:00:00.000Z"},{"id":"2610.40453","title":"PhysInsert: Physically Plausible Object Insertion with Geometric Priors and Bidirectional Object-Scene Interactions","summary":"We present PhysInsert, a framework for physically plausible reference-based object insertion that jointly adapts objects and their surrounding scenes. Existing methods often rely on masked inputs, generation-based pairs, or training pairs with limited viewpoint variation, leaving pose and illumination adaptation and bidirectional object-scene interactions insufficiently constrained. We construct the Insert World dataset with strictly aligned Blender-rendered counterfactual pairs and object references across viewpoints and lighting conditions. Building on these data, Geometry-Guided Adaptation couples a VGGT geometry stream with image and text streams under target-scene feature supervision to guide spatially consistent insertion. Complementary 2D Object-Scene Spatial Supervision jointly supervises object occupancy and environmental changes while grounding spatial instructions in object regions through image-text attention. We introduce PhysInsert-Bench with dedicated cross-environment and cross-view cases requiring illumination and pose adaptation beyond appearance copying. Experiments on PhysInsert-Bench and DreamBooth demonstrate improved image fidelity; a user study further shows more accurate placement and improved lighting integration, perspective, and pose plausibility.","upvotes":12,"at":"2026-10-28T20:00:00.000Z"},{"id":"2610.39663","title":"AB-Attention: Accurate Block Attention for Accelerating Video Diffusion Transformers","summary":"Video diffusion transformers generate high-quality videos, but the quadratic cost of 3D full attention dominates their inference. Dynamic block-sparse attention reduces this cost by computing exact attention only on key/value blocks selected from current activations. However, its fidelity degrades under aggressive sparsity: blocks are scored from coarse summaries or formed by costly global clustering, pruned blocks vanish from the softmax, and whole tiles are kept or dropped even when only a few of their keys matter. To address these limitations, we propose Accurate Block Attention (AB-Attention), a training-free method that accurately approximates dense attention at high sparsity. Its Intra-Block Clustering Selector (IBCS) estimates block importance faithfully yet cheaply by clustering queries only within each query block, without permuting tokens. Sampling with Inclusion-Probability Weighting (SIP) then samples blocks in proportion to these estimates and reweights them by inverse inclusion probability, so that one pass estimates dense attention rather than truncating it. Finally, its kernel gathers key/value blocks much smaller than a tile into full tensor-core tiles, refining selection at no extra tensor-core cost. On Wan2.1 and HunyuanVideo, AB-Attention skips 89--92\\% of attention, whereas the baselines compute 1.8--3.6times as much, yet it still improves PSNR by up to 4.4~dB over the strongest baseline and achieves up to 2.23times end-to-end speedup. Our code is available at https://github.com/InfRec-Cardinal/AB-Attention.","upvotes":4,"at":"2026-10-28T20:00:00.000Z"},{"id":"2610.38331","title":"Multi-Token Prediction Done Right","summary":"Multi-Token Prediction (MTP) is now a common ingredient in language-model pre-training, as it improves downstream performance and enables faster inference through speculative decoding. We identify a flaw in standard MTP training, in which a residual component of the MTP gradient interferes with both next-token and multi-token learning, increasingly so with scale. Specifically, the MTP cross-entropy gradient can be decomposed exactly into a KL self-distillation term that pulls each MTP head toward the model's own next-token prediction (NTP) distribution and improves speculative decoding, and a residual that has a low signal-to-noise ratio and buries the useful KL signal. We introduce the KL gradient-recovery trick: when two distributions share a linear output head, the gradient of their KL divergence can be recovered exactly by combining their cross-entropy gradients through straight-through gradient routing. Unlike minimizing the KL directly, this trick requires no vocabulary-sized logit matrices and is compatible with standard fused cross-entropy kernels. This enables Soft Multi-Token Prediction (Soft-MTP), a one-line code change to MTP that removes the noisy residual and backpropagates only the KL self-distillation signal by reusing the existing NTP and MTP cross-entropy computations. Soft-MTP supports both DeepSeek- and Meta-style MTP, adds no parameters, and incurs no measurable overhead. From 167M to 1.2B parameters, Soft-MTP improves over standard MTP with lower NTP perplexity, faster speculative decoding and higher zero-shot accuracy at every scale, lower MTP perplexity from 689M onward, and stronger math and code reasoning after fine-tuning at 1.2B. Soft-MTP also matches, if not exceeds, direct KL divergence minimization on most metrics. Soft-MTP is, in effect, a free lunch for MTP optimization. Our code can be found at https://github.com/JaydenTeoh/Soft-MTP.","upvotes":10,"at":"2026-10-27T20:00:00.000Z"},{"id":"2610.40647","title":"Read, Allocate, Route: Role-Coupled Tokenization for Pixel-Space Generative Transformers","summary":"Pixel-space generative Transformers typically use one token per image patch, so capturing finer detail requires more tokens and makes global attention increasingly expensive. We observe that finer detail does not necessarily require more global communication: low-frequency structure benefits from global exchange, while high-frequency detail can be modeled locally. Based on this insight, we introduce SpecSlot, a pixel-space rectified-flow Transformer that decouples output detail from the length of the global attention sequence. SpecSlot expands each spatial anchor into multiple slots, each assigned a disjoint band of DCT coefficients; this separates anchor density from spectral subdivision. Only the lowest-frequency slot from each anchor participates in global attention, while all slots interact locally. This allows the model to increase spectral detail without enlarging the global sequence. A spatial head further adds a local correction to the spectral prediction, while the generative state, target, and loss remain in pixel space. On ImageNet-256, SpecSlot achieves a comparable final FID to a FLOPs-matched JiT control while converging faster. On ImageNet-512, it improves FID by 2.84 over the control with 3.9% fewer FLOPs, and comes within only 0.69 FID of JiT-B/16 at 38.2% lower cost. On text-to-image generation, SpecSlot surpasses JiT-B/8 on DPG-Bench by 1.06 at 40.6% lower forward cost.","upvotes":34,"at":"2026-10-28T20:00:00.000Z"},{"id":"2610.40614","title":"L3G-World: Real-Time Infinite World Generation with Latent 3D Gaussian Memory","summary":"Interactive world models generate visual content conditioned on user actions, yet maintaining spatial consistency over long-horizon exploration in real time remains challenging. Existing approaches rely on either temporal context or explicit 3D memory, both of which scale poorly because memory grows with the length of the generated stream. Moreover, explicit 3D memories condition latent video generators by re-encoding RGB renderings, which precludes real-time generation. In this paper, we introduce a Latent 3D Gaussian Memory (L3G) that combines the efficiency of feed-forward reconstruction with a compact, rapidly renderable scene representation. Building on this memory, we propose L3G-World, in which a memory updater and a video diffusion transformer (DiT) alternate autoregressively to generate spatially consistent worlds. The memory updater spawns Gaussians in newly observed regions and refines existing ones, so that the memory grows with scene extent rather than stream length. Each Gaussian further stores a VAE latent feature, allowing the memory to be rendered directly into the DiT's latent space without VAE re-encoding. Experiments show that L3G-World achieves state-of-the-art generation quality and long-horizon spatial consistency, with up to 38.6times fewer 3D primitives, 3.2times faster memory updates, and 359times faster memory rendering relative to the most efficient prior 3D-memory baseline for each metric, enabling real-time world generation at 9 FPS.","upvotes":35,"at":"2026-10-28T20:00:00.000Z"},{"id":"2610.38342","title":"Investor's Last Exam: Can AI Agents Do Equity Investment Without Seeing the Future?","summary":"Financial agents must form forecasts and investment judgments from decision-time evidence, with predictions evaluated against subsequent outcomes. Existing benchmarks assess financial knowledge, research workflows, or forecasting, but evaluating the full research process requires a strictly frozen information set and professional evaluation criteria. We introduce Investor's Last Exam (ILE), an equity-research benchmark led by experienced buy-side investors and fund managers. Its three components are a versioned financial database spanning 3.43 million document versions and nine source categories, supplemented by structured market data, with validated timestamps governing retrieval and computation; a task taxonomy organized around professional analyst deliverables; and an expert-led framework linking scoring criteria to sources, calculations, and assumptions through financial dependency graphs. Evaluation spans 86 assignments and jointly assesses research quality, forecast accuracy, and simulated investment performance. GPT-6 Astra and Claude Opus 5.5 lead overall at 68.1 and 66.9 out of 100, yet even Astra scores only 52.5 on Forecast & Model. Our results show that even state-of-the-art models fail to translate overall research capability into predictive performance. ILE shifts the evaluation unit from isolated capabilities to the evidence--assumption--judgment chain, allowing researchers to locate where analytical advantages fail to translate into forecasts and investment judgments, and providing a traceable, diagnostic foundation for training and evaluating financial agents.","upvotes":9,"at":"2026-10-27T20:00:00.000Z"},{"id":"2610.39264","title":"Online Dense Reinforcement Learning with Planning-Grounding Agent for UI Navigation on Live IOS Apps","summary":"While frontier vision-language models demonstrate strong capabilities in user interface (UI) navigation, their high inference costs prohibit everyday use. Conversely, smaller models trained via supervised fine-tuning (SFT) or offline reinforcement learning often memorize static trajectories, leading to brittle execution and an inability to recover from errors. To address these limitations, we propose a novel online reinforcement learning framework that trains UI navigation agents directly on live iOS applications. Our framework decouples the agent into two specialized components: a Planning agent and a UI Grounding model. We rely on an LLM-as-a-Judge to provide comprehensive reward signals, combining dense, step-level feedback to evaluate intermediate progress with a terminal reward for overall task completion. To effectively leverage this dual reward structure, we optimize the agent using Proximal Policy Optimization (PPO) with step-level advantage estimation. Furthermore, we introduce GiGPPO, a novel neighbor-conditioned value estimation method that improves critic stability by leveraging reward signals from visually similar states across concurrent trajectories. To support this online training paradigm at scale, we introduce an efficient infrastructure that orchestrates hundreds of concurrent virtual iOS devices. Evaluations on rigorous benchmarks, including UINavBench, iOSWorld, and AndroidWorld, demonstrate that our online RL trained agents substantially outperform both SFT and terminal-only RL baselines while accelerating training convergence. Notably, our 8B RL agents achieve nearly a sevenfold improvement over the SFT baseline in out-of-distribution generalization to Android environments, while outperforming Gemini-3.5-Flash and tying with Gemini-3.1-Pro on real iOS applications.","upvotes":7,"at":"2026-10-28T20:00:00.000Z"}],"arxiv":[{"t":"Elucidating the Design Space of LM Data Attribution","url":"https://arxiv.org/abs/2610.40655v1","at":"2026-10-29T17:59:53.000Z","src":"arXiv"},{"t":"Propose-Filter-Release: Scaling Noise to Typical Sensitivity via Private Filtering","url":"https://arxiv.org/abs/2610.40654v1","at":"2026-10-29T17:59:49.000Z","src":"arXiv"},{"t":"Rethinking Neural Thickets Without Weight Perturbations","url":"https://arxiv.org/abs/2610.40649v1","at":"2026-10-29T17:59:29.000Z","src":"arXiv"},{"t":"Read, Allocate, Route: Role-Coupled Tokenization for Pixel-Space Generative Transformers","url":"https://arxiv.org/abs/2610.40647v1","at":"2026-10-29T17:59:21.000Z","src":"arXiv"},{"t":"Adaptive Scenario Generation for KL-DRO Around a Learned Density Model","url":"https://arxiv.org/abs/2610.40644v1","at":"2026-10-29T17:59:01.000Z","src":"arXiv"},{"t":"Luggage re-identification: A study of strategies towards a real-world application","url":"https://arxiv.org/abs/2610.40643v1","at":"2026-10-29T17:58:59.000Z","src":"arXiv"},{"t":"From extreme weather to agricultural consequences with agentic AI","url":"https://arxiv.org/abs/2610.40641v1","at":"2026-10-29T17:58:53.000Z","src":"arXiv"},{"t":"world2vec: Mechanistic world models through latent dynamics learning","url":"https://arxiv.org/abs/2610.40637v1","at":"2026-10-29T17:58:23.000Z","src":"arXiv"},{"t":"Probing Spatial State Representations in Visual Foundation Models","url":"https://arxiv.org/abs/2610.40636v1","at":"2026-10-29T17:58:16.000Z","src":"arXiv"},{"t":"ShallowDefend: Harnessing Shallow Safety Alignment for Multi-Turn Defender","url":"https://arxiv.org/abs/2610.40627v1","at":"2026-10-29T17:56:53.000Z","src":"arXiv"},{"t":"Testimony from Timelines: Evidence-Grounded Significance Testing of Temporal Clinical Hypotheses","url":"https://arxiv.org/abs/2610.40619v1","at":"2026-10-29T17:55:30.000Z","src":"arXiv"},{"t":"An ODE-based Sampler for Discrete Diffusion Models","url":"https://arxiv.org/abs/2610.40618v1","at":"2026-10-29T17:55:25.000Z","src":"arXiv"},{"t":"Difficulty Calibration Across Time for Membership Inference in Diffusion Models","url":"https://arxiv.org/abs/2610.40616v1","at":"2026-10-29T17:54:54.000Z","src":"arXiv"},{"t":"TextSeal2: Distortion-Free LLM Watermarking with Copula Mixing and Memoryless Races","url":"https://arxiv.org/abs/2610.40613v1","at":"2026-10-29T17:54:12.000Z","src":"arXiv"},{"t":"When to Assist and What to Suggest: Joint Gating and Memory-Grounded Hint Generation in One Compact Decoder","url":"https://arxiv.org/abs/2610.40611v1","at":"2026-10-29T17:54:07.000Z","src":"arXiv"},{"t":"Collaborative AI agents for autonomous experimental research with solid-state quantum spins","url":"https://arxiv.org/abs/2610.40589v1","at":"2026-10-29T17:47:50.000Z","src":"arXiv"},{"t":"Pre-Crime for Agents, at the Last Second: Probing When a Recruited Agent Gives In","url":"https://arxiv.org/abs/2610.40588v1","at":"2026-10-29T17:47:40.000Z","src":"arXiv"},{"t":"Multistatic ISAC with Deep Learning-Based Clutter Suppression for 5G and Beyond","url":"https://arxiv.org/abs/2610.40586v1","at":"2026-10-29T17:47:02.000Z","src":"arXiv"},{"t":"VeriCUA: Verification as Agentic Exploration for Computer-Using Agents","url":"https://arxiv.org/abs/2610.40578v1","at":"2026-10-29T17:45:26.000Z","src":"arXiv"},{"t":"Multi-View MoE: Letting Every Router Shape Expert Computation","url":"https://arxiv.org/abs/2610.40577v1","at":"2026-10-29T17:45:12.000Z","src":"arXiv"},{"t":"PIVOT: Progressive Intervention via Online Teacher Correction","url":"https://arxiv.org/abs/2610.40576v1","at":"2026-10-29T17:45:07.000Z","src":"arXiv"},{"t":"Every Token Needs Its Own Teacher: Multi-teacher Adaptive On-policy Self-distillation","url":"https://arxiv.org/abs/2610.40574v1","at":"2026-10-29T17:44:42.000Z","src":"arXiv"},{"t":"Gym-Anything for Robotics: Scaling physical simulations with digital agents","url":"https://arxiv.org/abs/2610.40565v1","at":"2026-10-29T17:42:32.000Z","src":"arXiv"},{"t":"Training Foundation Models with a Single Algorithm","url":"https://arxiv.org/abs/2610.40564v1","at":"2026-10-29T17:42:05.000Z","src":"arXiv"},{"t":"Comparing Human Role-Players and LLM-Simulated Clients in Online Counselling Training: An Analysis of Counselling Patterns","url":"https://arxiv.org/abs/2610.40563v1","at":"2026-10-29T17:41:59.000Z","src":"arXiv"}],"producthunt":[{"name":"Empacer","tagline":" The voice-first assistant for small business 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complete","votes":264,"comments":21,"url":"https://www.producthunt.com/products/speakon?utm_campaign=producthunt-api&utm_medium=api-v2&utm_source=Application%3A+Upcurrent+%28ID%3A+302252%29","at":"2026-10-30T07:01:00Z"},{"name":"Floot in ChatGPT","tagline":"Build and publish real apps without leaving ChatGPT","votes":206,"comments":59,"url":"https://www.producthunt.com/products/floot?utm_campaign=producthunt-api&utm_medium=api-v2&utm_source=Application%3A+Upcurrent+%28ID%3A+302252%29","at":"2026-10-29T07:01:00Z"},{"name":"MCP Server by XWiki","tagline":"Give your AI your wiki's knowledge while keeping the control","votes":169,"comments":38,"url":"https://www.producthunt.com/products/xwiki?utm_campaign=producthunt-api&utm_medium=api-v2&utm_source=Application%3A+Upcurrent+%28ID%3A+302252%29","at":"2026-10-30T07:01:00Z"},{"name":"Gemini 4 Carbon","tagline":"Google's next frontier model","votes":164,"comments":7,"url":"https://www.producthunt.com/products/gemini-4-carbon?utm_campaign=producthunt-api&utm_medium=api-v2&utm_source=Application%3A+Upcurrent+%28ID%3A+302252%29","at":"2026-10-29T07:01:00Z"},{"name":"Gologin Cloud Browser","tagline":"Cloud browser for AI agents and tough sites ","votes":159,"comments":29,"url":"https://www.producthunt.com/products/gologin?utm_campaign=producthunt-api&utm_medium=api-v2&utm_source=Application%3A+Upcurrent+%28ID%3A+302252%29","at":"2026-10-29T07:01:00Z"},{"name":"Canva Brush","tagline":"A vision-language model that edits your designs","votes":152,"comments":6,"url":"https://www.producthunt.com/products/canva?utm_campaign=producthunt-api&utm_medium=api-v2&utm_source=Application%3A+Upcurrent+%28ID%3A+302252%29","at":"2026-10-30T07:01:00Z"},{"name":"TopAgent","tagline":"Drive B2B event attendance with an AI agent","votes":134,"comments":15,"url":"https://www.producthunt.com/products/topmessage?utm_campaign=producthunt-api&utm_medium=api-v2&utm_source=Application%3A+Upcurrent+%28ID%3A+302252%29","at":"2026-10-29T07:01:00Z"},{"name":"fastbrowse","tagline":"Fastest open source browser agent","votes":129,"comments":11,"url":"https://www.producthunt.com/products/fastbrowse?utm_campaign=producthunt-api&utm_medium=api-v2&utm_source=Application%3A+Upcurrent+%28ID%3A+302252%29","at":"2026-10-30T07:01:00Z"},{"name":"Chopstick","tagline":"Mac activity and action widgets that write themselves","votes":126,"comments":13,"url":"https://www.producthunt.com/products/chopstick?utm_campaign=producthunt-api&utm_medium=api-v2&utm_source=Application%3A+Upcurrent+%28ID%3A+302252%29","at":"2026-10-30T07:01:00Z"},{"name":"Radar","tagline":"You don't need an AI SRE. Give your agent Radar.","votes":125,"comments":22,"url":"https://www.producthunt.com/products/radar-7?utm_campaign=producthunt-api&utm_medium=api-v2&utm_source=Application%3A+Upcurrent+%28ID%3A+302252%29","at":"2026-10-29T07:01:00Z"},{"name":"IFTTT.ai","tagline":"The easiest way to automate anything","votes":115,"comments":10,"url":"https://www.producthunt.com/products/ifttt?utm_campaign=producthunt-api&utm_medium=api-v2&utm_source=Application%3A+Upcurrent+%28ID%3A+302252%29","at":"2026-10-29T07:01:00Z"},{"name":"Teable Mobile","tagline":"Build the systems your team runs on, now from your phone","votes":112,"comments":12,"url":"https://www.producthunt.com/products/teable-4?utm_campaign=producthunt-api&utm_medium=api-v2&utm_source=Application%3A+Upcurrent+%28ID%3A+302252%29","at":"2026-10-30T07:01:00Z"},{"name":"Viggle JST-2","tagline":"JST-2: 3D scenes, characters and motion from one model","votes":111,"comments":2,"url":"https://www.producthunt.com/products/viggle-ai?utm_campaign=producthunt-api&utm_medium=api-v2&utm_source=Application%3A+Upcurrent+%28ID%3A+302252%29","at":"2026-10-29T07:01:00Z"},{"name":"Intrascope 2.0","tagline":"New AI agent, 1,650+ apps, Skills and smart model routing","votes":106,"comments":18,"url":"https://www.producthunt.com/products/intrascope-app?utm_campaign=producthunt-api&utm_medium=api-v2&utm_source=Application%3A+Upcurrent+%28ID%3A+302252%29","at":"2026-10-29T07:01:00Z"},{"name":"Faylee","tagline":"Bug reports your coding agent can fix from one link","votes":98,"comments":5,"url":"https://www.producthunt.com/products/faylee?utm_campaign=producthunt-api&utm_medium=api-v2&utm_source=Application%3A+Upcurrent+%28ID%3A+302252%29","at":"2026-10-30T07:01:00Z"},{"name":"Voxel51 Multimodal","tagline":"Better data. Smarter robots. Repeat.","votes":94,"comments":21,"url":"https://www.producthunt.com/products/voxel51?utm_campaign=producthunt-api&utm_medium=api-v2&utm_source=Application%3A+Upcurrent+%28ID%3A+302252%29","at":"2026-10-29T07:01:00Z"},{"name":"SparkBots AI (Powered by Jev)","tagline":"Instantly extract key details and analyze any webpage or PDF","votes":91,"comments":9,"url":"https://www.producthunt.com/products/sparkbots-ai-analyze-pages-and-pdfs?utm_campaign=producthunt-api&utm_medium=api-v2&utm_source=Application%3A+Upcurrent+%28ID%3A+302252%29","at":"2026-10-30T07:01:00Z"}],"hn":[{"src":"Hacker News","t":"AI isn't replacing SREs, it's deskilling them","url":"https://news.ycombinator.com/item?id=50305693","link":"https://newsletter.signoz.io/p/ai-isnt-replacing-sres-its-deskilling","up":516,"com":386,"at":"2026-10-30T15:52:18.000Z"},{"src":"Hacker News","t":"Proton's CEO Cancels AI 'Re-Interviews'","url":"https://news.ycombinator.com/item?id=50307183","link":"https://www.404media.co/protons-ceo-cancels-ai-re-interviews/","up":263,"com":221,"at":"2026-10-30T17:24:45.000Z"},{"src":"Hacker News","t":"US tech stocks see sharp falls over AI cost fears","url":"https://news.ycombinator.com/item?id=50306008","link":"https://www.bbc.co.uk/news/articles/cvwy72e0n72yo","up":242,"com":266,"at":"2026-10-30T16:13:02.000Z"},{"src":"Hacker News","t":"Ask HN: Are you tired too in this AI era?","url":"https://news.ycombinator.com/item?id=50301479","link":"","up":142,"com":139,"at":"2026-10-30T09:17:43.000Z"},{"src":"Hacker News","t":"Cisco gives engineer unlimited AI tokens to rewrite legacy code","url":"https://news.ycombinator.com/item?id=50304122","link":"https://fortune.com/2026/10/30/cisco-president-jeetu-patel-gen-z-27-year-old-unlimited-ai-tokens-rewrite-legacy-code-entry-level-jobs/","up":115,"com":129,"at":"2026-10-30T14:08:20.000Z"},{"src":"Hacker News","t":"Neal Stephenson on AI and What Comes Next [video]","url":"https://news.ycombinator.com/item?id=50304280","link":"https://www.youtube.com/watch?v=Ii-aeW5pHPQ","up":51,"com":23,"at":"2026-10-30T14:18:34.000Z"},{"src":"Hacker News","t":"Fiber lasts decades, GPUs last three years: the AI boom isn't the dot-com boom [video]","url":"https://news.ycombinator.com/item?id=50302407","link":"https://www.youtube.com/watch?v=MENoB3Rex28","up":41,"com":53,"at":"2026-10-30T11:16:15.000Z"},{"src":"Hacker News","t":"Wall Street closes sharply lower as AI cash burn fears spark tech selloff","url":"https://news.ycombinator.com/item?id=50310431","link":"https://www.reuters.com/business/nasdaq-futures-tumble-amazon-intel-results-deepen-ai-spending-woes-2026-10-30/","up":41,"com":5,"at":"2026-10-30T21:32:35.000Z"},{"src":"Hacker News","t":"Some Thing is Happening A note from the AI 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