Bezos Earth Fund Announces $30 Million in AI Grand Challenge Awards - Bezos Earth Fund
<a href="https://news.google.com/rss/articles/CBMitgFBVV95cUxOS1dBMVQweUFMY29EUHZMcC0wWVBzRElrdkloMGtFZ2RjT1ZPZXpGR0VwdzRqbFNOMWhBdkJhanVFem1VSDZpT0Z5RXBwNy1DaDV2UTQ0LTMybVVvWlB2N25pWlV2ZnFDaVJ2SzVKV2FQbjRnczZRM2FBNHJvZXNWT3Jya2FNdVdSYmpRUVMtdkR1QVp6Q0doSzhhSDZJM3RjNUF4dnFocUNVeDBTSW51eHdhRW95Zw?oc=5" target="_blank">Bezos Earth Fund Announces $30 Million in AI Grand Challenge Awards</a> <font color="#6f6f6f">Bezos Earth Fund</font>
Could not retrieve the full article text.
Read on GNews AI climate →Sign in to highlight and annotate this article
Conversation starters
Daily AI Digest
Get the top 5 AI stories delivered to your inbox every morning.
More about
announcemillion
Execution-Verified Reinforcement Learning for Optimization Modeling
arXiv:2604.00442v1 Announce Type: new Abstract: Automating optimization modeling with LLMs is a promising path toward scalable decision intelligence, but existing approaches either rely on agentic pipelines built on closed-source LLMs with high inference latency, or fine-tune smaller LLMs using costly process supervision that often overfits to a single solver API. Inspired by reinforcement learning with verifiable rewards, we propose Execution-Verified Optimization Modeling (EVOM), an execution-verified learning framework that treats a mathematical programming solver as a deterministic, interactive verifier. Given a natural-language problem and a target solver, EVOM generates solver-specific code, executes it in a sandboxed harness, and converts execution outcomes into scalar rewards, opti
Decision-Centric Design for LLM Systems
arXiv:2604.00414v1 Announce Type: new Abstract: LLM systems must make control decisions in addition to generating outputs: whether to answer, clarify, retrieve, call tools, repair, or escalate. In many current architectures, these decisions remain implicit within generation, entangling assessment and action in a single model call and making failures hard to inspect, constrain, or repair. We propose a decision-centric framework that separates decision-relevant signals from the policy that maps them to actions, turning control into an explicit and inspectable layer of the system. This separation supports attribution of failures to signal estimation, decision policy, or execution, and enables modular improvement of each component. It unifies familiar single-step settings such as routing and a
In harmony with gpt-oss
arXiv:2604.00362v1 Announce Type: new Abstract: No one has independently reproduced OpenAI's published scores for gpt-oss-20b with tools, because the original paper discloses neither the tools nor the agent harness. We reverse-engineered the model's in-distribution tools: when prompted without tool definitions, gpt-oss still calls tools from its training distribution with high statistical confidence -- a strong prior, not a hallucination. We then built a native harmony agent harness (https://github.com/borislavmavrin/harmonyagent.git) that encodes messages in the model's native format, bypassing the lossy Chat Completions conversion. Together, these yield the first independent reproduction of OpenAI's published scores: 60.4% on SWE Verified HIGH (published 60.7%), 53.3% MEDIUM (53.2%), and
Knowledge Map
Connected Articles — Knowledge Graph
This article is connected to other articles through shared AI topics and tags.
More in Releases
TIPA launches AI innovation team to drive SME AX in South Korea - CHOSUNBIZ - Chosunbiz
<a href="https://news.google.com/rss/articles/CBMiggFBVV95cUxQaVZrNFVSRkFoajlPWmVLU3h3MEhkdE1STk8tYzB0NDVUaDV5LTl1c3c5WmpjS1lSNFUya3JhWVJZam1sa3JKcTZITkdJeHd6cEZkMWVpajEzVkdTN2k4WjJhVjExLWtXcUE0TG9nOFRWQW1XY2hzWnp3ZndQWXFIZFVn0gGWAUFVX3lxTFBFdXlrUjgyYUlGUnd6RmVXOGVsQWJGVHcwX1AydmF5cHZKSFBrM0hXZ01KZWtUYmFTdU5DaWducGo0amx2T0pndzZhel9qYm5GOFpEYlVmeWVLZG5MeC1yTUF1Q3dfdWtxQ0RoM0x2SGtjR0lwUUZCbVRsbjFWeFV0WW16eG04TXJKQzdFRnBoVEJQN3JjZw?oc=5" target="_blank">TIPA launches AI innovation team to drive SME AX in South Korea - CHOSUNBIZ</a> <font color="#6f6f6f">Chosunbiz</font>
ParetoBandit: Budget-Paced Adaptive Routing for Non-Stationary LLM Serving
arXiv:2604.00136v1 Announce Type: new Abstract: Production LLM serving often relies on multi-model portfolios spanning a ~530x cost range, where routing decisions trade off quality against cost. This trade-off is non-stationary: providers revise pricing, model quality can regress silently, and new models must be integrated without downtime. We present ParetoBandit, an open-source adaptive router built on cost-aware contextual bandits that is the first to simultaneously enforce dollar-denominated budgets, adapt online to such shifts, and onboard new models at runtime. ParetoBandit closes these gaps through three mechanisms. An online primal-dual budget pacer enforces a per-request cost ceiling over an open-ended stream, replacing offline penalty tuning with closed-loop control. Geometric fo
AI-Mediated Explainable Regulation for Justice
arXiv:2604.00237v1 Announce Type: cross Abstract: Present practice of deciding on regulation faces numerous problems that make adopted regulations static, unexplained, unduly influenced by powerful interest groups, and stained with a perception of illegitimacy. These well-known problems with the regulatory process can lead to injustice and have substantial negative effects on society and democracy. We discuss a new approach that utilizes distributed artificial intelligence (AI) to make a regulatory recommendation that is explainable and adaptable by design. We outline the main components of a system that can implement this approach and show how it would resolve the problems with the present regulatory system. This approach models and reasons about stakeholder preferences with separate pref
When and Where: A Model Hippocampal Network Unifies Formation of Time Cells and Place Cells
arXiv:2604.00036v1 Announce Type: cross Abstract: Hippocampal place and time cells encode spatial and temporal aspects of experience. Both have the same neural substrate, but have been modeled as having different functions and mechanistic origins, place cells as continuous attractors, and time cells as leaky integrators. Here, we show that both types emerge from two dynamical regimes of a single recurrent network (RNN) modeling hippocampal CA3 as a predictive autoencoder. The network receives simulated, partially occluded ``experience vectors" containing spatial patterns (location-specific activity sampled during environmental traversal) and/or temporal patterns (correlated activity pairs separated by ``void" intervals), and is trained to reconstruct missing input. During spatial navigatio
Discussion
Sign in to join the discussion
No comments yet — be the first to share your thoughts!