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Top story

Introducing GPT-6.1 Terra

heat 95

Market signal

Will Z.ai have the best AI model at the end of June 2026?

0% / Polymarket

Research

Selective Tie Learning: Mitigating Spurious Correlations in Preference Optimization

arXiv

Launch

Fundraisly

+100 momentum

Product surface

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Each module is designed to become dynamic through source ingestion, scoring, human curation, and realtime updates.

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Rank new AI products, agents, models, APIs, and open-source projects by real-world usefulness.

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Track AI-related prediction markets, probability moves, volumes, and event risk.

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Curate news, podcasts, interviews, newsletters, and video into concise signal briefs.

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Summarize papers, model cards, benchmarks, evals, and safety reports for practitioners.

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Map public and private AI exposure across compute, cloud, apps, infra, energy, and data.

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Follow funding rounds, valuation changes, strategic investments, and ecosystem concentration.

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Compare frontier, open-weight, coding, reasoning, multimodal, and specialist AI models.

Launch rankings

Daily AI launches ranked by momentum.

Product Hunt-style discovery for AI tools, models, agents, APIs, and open-source releases.

999

Fundraisly

hot

Product Hunt / 1,488 votes

AI fundraising agent that finds investors and books meetings

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+100
999

Tencent EdgeOne Makers

hot

Product Hunt / 1,155 votes

Ship AI agents like web apps, in minutes.

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+100
999

Pazi

hot

Product Hunt / 991 votes

Vibe code business operations

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+100

Prediction markets

AI bets, odds, and probability moves in one dashboard.

Media feed

News, podcasts, and interviews without the noise.

News

Introducing GPT-6.1 Terra

Bringing fast and capable computer use to everyone

News

How Ultrafast cut incident investigation from hours to minutes at OpenAI

See how OpenAI engineers use Codex Ultrafast to investigate incidents in minutes. Explore faster security analysis, code refactoring, and daily workflows.

News

How Fanatics and OpenAI streamline the fan experience

Fanatics uses OpenAI to help fans complete their game-day outfits with Shop the Look. Testing across roughly 30,000 MLB products showed 10% higher engagement.

Research corner

Papers, model cards, and evals translated for operators.

arXiv

Selective Tie Learning: Mitigating Spurious Correlations in Preference Optimization

Preference optimization can learn spurious correlations, and augmenting the preference data with ties (equally preferred responses) can reduce spurious reliance. However, ties differ in their corrective effect, making their selection for augmentation important. For log-linear policies, we derive each tie's influence on spurious reliance and show that taking the largest predicted reductions is first-order optimal among subsets of equal size. Computing these influences requires knowledge of the spurious features and the curvature of the training objective, which is often unavailable or expensive to estimate. To this end, we explore using the magnitude of a tie's implicit reward margin and char

arXiv

Quantifying Claim Smoothing by LLM Research Agents

Deep research, or any use of large language models (LLMs) to read a large document collection and produce insights, requires synthesizing information from different sources. In this work, we find that LLMs produce long-form research reports that read as confident syntheses but which fail to accurately reflect the underlying documents. We call this failure smoothing. In collapsing diverse and potentially conflicting sources into a single narrative, the model discards the evidential "jaggedness" of its corpus and asserts conclusions more decisive than the citations can support. We introduce a claim level scoring pipeline, SmoothScore, to quantify this effect. Our pipeline extracts key conclusi

arXiv

FOCUS: Active Critic Learning for Long-Horizon Language Model Agents

Sparse outcome rewards make it difficult for long-horizon language-model agents to assign credit to earlier actions. Learned critics provide value estimates for intermediate states, but these estimates can be inaccurate, while estimating intermediate values with repetitive continuation samples from points within a trajectory incurs substantial costs. To mitigate these issues, we introduce FOCUS, an RL method that uses selective branching to help the critic network learn its value estimator. The rollout controller facilitates active learning for the critic by sampling continuations from states that are expected to most reduce the critic's estimation error. These refined value estimates then s

arXiv

Game theory of academic effort and hiring in the age of AI

Many academic disciplines (in particular, mathematics and the theoretical sciences) face difficult questions about how to adapt their practices to the rise of highly capable artificial intelligence (AI). Before AI, a well-written research paper with a strong theoretical result acted as a reliable signal that its authors understood their field in depth. Now that AI is widely accessible, such signals have become cheaper to produce. As a result, they no longer reliably sort academics by their level of understanding. This has direct consequences for academic hiring. The age of AI raises a question for each side of the academic job market. Should an early-career researcher invest in a deep unders

arXiv

Learning When to Commit: Schedule Distillation for Fast Diffusion Language Models

Diffusion large language models (dLLMs) can generate multiple tokens in parallel, but often require many decoding rounds to complete a response. Efficient generation depends not only on which tokens are easy to predict, but also on how committing to them helps predict the remaining positions. A highly predictable token may provide little useful context, while revealing a less predictable token can help several other positions become predictable sooner. We propose dSpark, a new schedule distillation-based framework for learning when to commit tokens for faster diffusion language generation. To achieve this, we construct unmasking schedules that prioritize tokens whose reveal makes other posit

arXiv

Does VGGT Know What It Does Not Know?

Feed-forward 3D reconstruction models such as VGGT regress cameras and geometry in a single forward pass, but they report no uncertainty for the camera poses they predict: an incorrect relative pose looks no different from a correct one. We ask whether the frozen model nonetheless knows when its pose is wrong. On HardMatch, a benchmark of 1,000 deliberately difficult image pairs on which VGGT fails on 35% of pairs, a linear probe on VGGT's frozen features predicts pose failure on held-out categories with ROC-AUC 93.5%, compared with 79.7% for VGGT's own point-map confidence and 77.7% for a classifier trained on all of VGGT's outputs. The probe catches failures VGGT is confident about, lets a

arXiv

Pretraining Knows What, Simulation Teaches How: Compositional Generalization in a Sim-Trained Robot Agent

A robot policy needs to understand what a task is asking for in a scene, and how to move the robot to accomplish that task. To achieve these two skills, we introduce Zero-real Agent Policy (ZAP), a recipe that teaches a small off-the-shelf vision-language model to control a robot via a simple tool harness. We use a pretrained 27B parameter model to bootstrap world understanding and reasoning, followed by supervised fine-tuning and GRPO on simple simulated tasks to teach it how to move the robot. Without any real-world robot data during training, ZAP reaches 96.7% success on real-world tasks, matching GPT-6 Astra in our harness and outperforming the MolmoAct2 VLA (33.3%), while running 3x fas

arXiv

Understanding Jev-Style System One Models: Does Structure Reduce Selection Bias?

Selection bias in large language models (LLMs) can persist despite instructions to ignore presentation order or prompts that elicit reasoning. Recent efforts to reproduce Jev-style System One behavior have produced a growing family of models that predict probabilities directly over supplied candidates. Yet systematic comparisons of selection bias across Jev versions, Jev reproductions, and generative LLMs remain limited in multi-option tasks. This leaves a central question: does Jev-style decision structure reduce selection bias? We address this question with a simple test that shuffles options while keeping question and candidate content fixed. Across two Jev versions, twelve Jev reproducti

arXiv

Reva: Real-Time Vulnerability Detection through Contextual Probing of Large Language Models

A vulnerability costs more to fix the later it is discovered. As coding agents generate code faster than developers can review it, software needs a first line of defense screening each new function in real time. LLM agents have recently become capable of finding real-world vulnerabilities by working as a security expert would: they run analysis tools, explore the surrounding code, test hypotheses and weigh the evidence before forming their own judgement. This reasoning, however, spans many model calls, so that each verdict takes minutes of compute, far too long for real-time screening. Faster detectors, which fine-tune deep learning models or prompt an LLM for a direct verdict, are less accu

arXiv

Profiling a Frontier Agent with EBench: From Direct Control to Harness with Policy

Frontier multimodal large language models (MLLMs) can control robots without robot-specific training, but how their capabilities compare and interact with post-trained vision-language-action (VLA) and world action model (WAM) policies across manipulation demands remains to be thoroughly evaluated. In this report, we evaluate GPT-6-Astra on EBench as the core of an agent that controls a robot through an agent harness under the same visual and proprioceptive observation protocol as post-trained policies. We study two agent configurations: direct control, in which the agent generates waypoint commands directly, and harness with policy, in which it can also invoke a post-trained VLA or WAM polic

arXiv

Information-Theoretic Secure Aggregation With Heterogeneous Client Quantization

In federated learning (FL), secure aggregation enables a server to compute the sum of local model updates of $N$ clients, without revealing any information on the individual model updates to the server, beyond their sum. Different clients may be subject to heterogeneous bandwidth constraints, and consequently may choose different numbers of quantization levels to represent the information they send. Most secure aggregation works require the clients to project the values of their update parameters to a common finite group (or field), which does not directly accommodate heterogeneous precision constraints on their transmitted masked updates. Existing approaches of secure aggregation with heter

arXiv

I-HITL: Inalienable Human-in-the-Loop

Human oversight is a pillar of AI safety. It is often exercised by a human in the loop (HITL) who approves critical actions taken by an AI agent. AI agents' increasing ability to bypass guardrails, though, raises the question of how to enforce HITL robustly. We introduce the concept of inalienable human-in-the-loop (I-HITL). An I-HITL protocol ensures that an agent can initiate an action only with explicit human approval, even when the human interacts with a system (e.g., a local agentic harness) compromised by the agent. A strong I-HITL protocol additionally ensures such approval even when an adversarial human attempts to delegate approvals to an agent. To motivate I-HITL, we report on expe

arXiv

Work Agents: Systems, Capabilities, and Optimization

As foundation models evolve from information generation to digital work execution, agents are increasingly capable of completing complex tasks across tools, applications, and workflows. However, existing research lacks a unified perspective on how agents reliably accomplish delegated work. To address this gap, we present a work-centric, system-level perspective that views work agents as systems combining foundation models and runtime harnesses to execute tasks in stateful workspaces and deliver verifiable outcomes. We introduce two complementary dimensions, work persistence and work openness, to characterize working conditions, and classify digital work into five forms according to task scop

arXiv

Preserve to Expand: Source-Aware Adaptation and Model Merging for Multilingual LLM-Based ASR

Expanding multilingual ASR systems to new languages is challenging when adaptation must preserve previously supported languages without replaying source-language training data. We study this problem in connector-based Speech LLMs, where a trainable connector maps speech representations into a frozen large language model. We evaluate target-update construction and merging strategies for adapting the connector while preserving source-language performance. In single-target expansion, target-only fine-tuning and standard TIES merging learn new languages but cause severe source-language forgetting, while SSU-Wanda+TIES gives the best source-preserving trade-off. In multi-target expansion, update

arXiv

Patient-specific rubric-based evaluation of large language models across longitudinal cancer care

Large language models (LLMs) show promise in specialty medicine, yet existing evaluations largely focus on isolated clinical encounters rather than tracking how clinical responsibilities evolve across a longitudinal care pathway. Here we introduce NPC-RubricBench, a phase-aware benchmark for evaluating multistage clinical reasoning across the cancer care continuum, using nasopharyngeal carcinoma as a defined disease context. Curated from longitudinal records of 405 patients across two cancer centers, the benchmark comprises 941 patient-phase units across six sequential care phases and 17,688 patient-specific atomic criteria spanning five evaluation dimensions, with each assessment strictly r

arXiv

On the stability of learning

Learning algorithms are usually analyzed under the assumption that informative gradients are continuously available. But this is not always true---learning signals in biological neural networks, for example, may be sparse, local, and only intermittently available at any given synapse. In this work, we show that if gradient information is noisy and only intermittently available, learning may be unstable. In particular, even if average learning dynamics converge to the correct optimum, variance in learning may be asymptotically infinite. We explore this point in a number of analytically tractable settings, including single-layer and multi-layer linear networks, and derive explicit conditions u

arXiv

Learning Force-Reactive Humanoid Loco-Manipulation

Many everyday tasks require controlling contact force as well as motion. On a humanoid, this force must often be coordinated across both hands and the whole body, and cannot be specified by kinematic trajectories alone. Therefore, we present FLORE, a framework for learning force-reactive humanoid loco-manipulation. We collect robot-free demonstrations using two force-sensing UMI-style handheld grippers for hand motion and contact wrenches, and wearable trackers for body motion. A force-reactive diffusion policy takes vision, proprioception, and measured wrenches as input, then predicts actions and per-hand force commands. A hybrid compliance whole-body controller with force tracking executes

arXiv

Auto-Research Arena: Benchmarking and Advancing Auto-Research in Language Modeling

As autonomous research systems become more capable, benchmarks must both evaluate their performance and support meaningful research progress. We introduce Auto-Research Arena, a multi-target benchmark for evaluating autonomous research systems and discovering ideas in language modeling. Arena supports diverse research objectives within a shared experimental environment by separating admissibility from improvement: model quality and resource constraints determine whether a result qualifies, while the selected target measures progress among admissible results. We instantiate Arena in language model pretraining with nine targets spanning model, data, training, and inference efficiency, and eval

arXiv

Center-of-Mass Interpretation of Evolution Strategies

Evolution strategies (ES) update a model by sampling a population of perturbed copies, scoring each one, and moving in the reward-weighted direction of the perturbations. This update is usually presented as an estimate of a smoothed gradient. We present a geometrical interpretation of this update instead. Treat each perturbed model as a particle and its reward as the particle's mass. Before scoring, every particle has the same mass and the population balances at its centroid. Once rewards are assigned, it balances at its reward-weighted center of mass instead. We show that the mean-subtracted ES update is exactly this displacement, scaled by a scalar factor that depends on the mean reward, f

arXiv

Retriever-Free Retrieval: Navigating Documents with Decision Models

While language models have become dramatically more capable, retrieval still largely relies on lexical matching or similarity in fixed embedding spaces. This creates a capability asymmetry: what the retriever fails to find, even the strongest generator cannot recover. We propose DecisionRAG, which brings the capabilities of pretrained language models to retrieval using decision models that jointly evaluate candidate branches in a single forward pass, without generating any tokens, and show that judging candidates in a shared context makes their relevance estimates comparable. Like a person browsing a library, DecisionRAG navigates the corpus's inherent structure from broad topics down to ind

Investment index

Public and private AI exposure, organized by the stack.

Compute

NVIDIA / AMD / Broadcom / TSMC / ASML / Arm

Accelerator supply, networking, packaging, and utilization remain the central AI bottleneck.

Cloud and platforms

Microsoft / Amazon / Google / Oracle / CoreWeave

Distribution and committed capacity shape which model companies can scale.

Applications

OpenAI / Anthropic / Perplexity / Runway / Harvey

The application layer is splitting into consumer assistants and vertical workflow systems.

Energy and infrastructure

Utilities / Data centers / Cooling / Storage / Networking

Power contracts and data center approvals are turning into AI growth indicators.

Realtime plan

Dynamic data from day one, editorial quality before automation.

Manual curation

Start with admin-approved seed data and daily editorial briefs.

Scheduled ingestion

Pull RSS, arXiv, market APIs, launch feeds, and company updates every 15 minutes.

Realtime surfaces

Use Supabase Realtime for odds changes, breaking items, and admin publish events.