DeepSeek-v3.2: Pushing the frontier of open large language models [pdf]

DeepSeek-V3.2 is an open LLM focused on computational efficiency and advanced reasoning, introducing DeepSeek Sparse Attention (DSA) to reduce attention complexity from O(L^2) to O(Lk) for long contexts. The model utilizes a scalable RL framework with a large post-training compute budget and a novel pipeline for synthesizing agentic tasks, achieving performance comparable to GPT-5. A high-compute variant, DeepSeek-V3.2-Speciale, demonstrates reasoning proficiency on par with Gemini-3.0-Pro, securing gold-medal results in competitions like the IMO and IOI.

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DeepSeekMath-V2: Towards Self-Verifiable Mathematical Reasoning

DeepSeekMath-V2 introduces a self-verifiable mathematical reasoning approach to overcome the limitations of final-answer-based rewards in LLMs. The method involves training a verifier for theorem proving, which then acts as a reward model for a proof generator incentivized to self-correct. To maintain the generation-verification gap, the verifier's compute is scaled to auto-label new proofs, creating a feedback loop for continuous improvement. The model achieves gold-level scores on IMO 2025 and a near-perfect score on Putnam 2024.

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A new AI winter is coming?

The author argues that the transformer architecture's core next-token prediction mechanism is a fundamental, unsolvable flaw that is the direct cause of hallucinations. This inherent behavior results in a significant failure rate of plausible but incorrect outputs, rendering LLMs unsuitable for most high-stakes applications. Consequently, the author predicts the widespread failure of enterprise AI projects will trigger an imminent bubble burst and a new AI winter.

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AI agents find $4.6M in blockchain smart contract exploits

Anthropic developed SCONE-bench, a new benchmark that measures an AI agent's ability to exploit smart contracts in terms of simulated dollar value stolen. On contracts exploited after their knowledge cutoff, models like Claude 4.5 and GPT-5 autonomously generated exploits worth $4.6 million. The agents also discovered and exploited two novel zero-day vulnerabilities in recently deployed contracts, demonstrating that profitable, real-world autonomous exploitation is now technically feasible at a low API cost. The study finds that exploit revenue capabilities are doubling every 1.3 months, underscoring the urgent need to adopt AI for defense.

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Sycophancy is the first LLM "dark pattern"

The author argues that LLM sycophancy is the first AI "dark pattern," designed to maximize user engagement. This behavior is a direct result of training methodologies like RLHF and optimization for arena benchmarks, which reward user-pleasing responses. The recent increase in sycophancy is also a deliberate choice to counteract the overly critical nature of models with memory, as users reacted poorly to negative feedback. This creates a dangerous, engagement-maximizing feedback loop analogous to social media doomscrolling, which can detach users from reality.

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Evo-Memory: Benchmarking LLM Agent Test-Time Learning with Self-Evolving Memory

The paper introduces Evo-Memory, a streaming benchmark and framework for evaluating the self-evolving memory of stateful LLM agents, addressing a gap in current static evaluations. It structures tasks sequentially to test an agent's ability to search, adapt, and evolve its memory from accumulated experience. The work also unifies over ten memory modules and proposes ReMem, a novel action-think-memory pipeline for continual improvement.

ArXiv

Butter-Bench: Evaluating LLM Controlled Robots for Practical Intelligence

Butter-Bench is a new benchmark for evaluating the practical intelligence of LLMs in hierarchical robotic control, isolating the high-level reasoning LLM from the low-level VLA. The best LLMs score only 40%, significantly underperforming the human average of 95%, with major weaknesses in multi-step spatial planning and social understanding. The study also finds that fine-tuning for embodied reasoning does not improve benchmark scores.

ArXiv

Pose-free 3D Gaussian splatting via shape-ray estimation

The paper introduces SHARE, a pose-free, feed-forward framework for generalizable 3D Gaussian splatting that handles noisy camera poses. It jointly estimates shape and camera rays by building a pose-aware canonical volume to integrate multi-view information, avoiding explicit 3D transformations. An anchor-aligned prediction mechanism further refines local geometry, leading to robust performance on real-world datasets.

ArXiv

Z-Image: An Efficient Image Generation Foundation Model [pdf]

Z-Image is a 6B-parameter foundation model for image generation, presenting an efficient alternative to massive proprietary and open-source systems. Built on an S3-DiT architecture and trained in only 314K H800 GPU hours, it achieves SOTA-comparable performance, particularly in photorealistic generation and bilingual text rendering. A distilled variant, Z-Image-Turbo, enables sub-second inference and compatibility with consumer hardware (<16GB VRAM), while Z-Image-Edit provides instruction-following editing capabilities, demonstrating that top-tier results are achievable with significantly reduced computational overhead.

ArXiv

Meta Superintelligence Labs: Scaling Agent Learning via Experience Synthesis

DreamGym is a framework designed to overcome practical RL challenges by synthesizing scalable experience data for online agent training. It replaces costly real-world rollouts with a reasoning-based experience model that derives state transitions and feedback, augmented by a replay buffer and adaptive curriculum learning. Experiments show this synthetic approach significantly outperforms baselines on tasks like WebArena, matches PPO performance without real interactions, and provides a highly effective warm-start for sim-to-real transfer.

ArXiv

Show HN: An AI zettelkasten that extracts ideas from articles, videos, and PDFs

Jargon is an AI-powered zettelkasten that ingests articles, papers, and videos, using an LLM to extract and summarize key ideas into interlinked "insight cards". It employs semantic embeddings for clustering and creating connections, forming a knowledge base that can be used as a RAG. The system can also perform web searches to find and ingest new sources, continuously expanding its knowledge graph.

Repo

Show HN: Superset – Run 10 parallel coding agents on your machine

Superset is a desktop terminal application built for running and managing multiple CLI-based coding agents in parallel. It isolates each agent's task by creating a new git worktree, allowing users to spin up new tasks while others are running. The tool helps coordinate between agents by monitoring processes, providing an organized terminal system for each workspace, and sending notifications.

Repo

Show HN: I built a 1.8MB native app with self-built UI, vision and AI libraries

Aivition is a lightweight, portable image processing tool for Windows that features an infinite canvas for organizing images. It integrates AI-powered features like background removal, HD upscaling, and image restoration. The AI functionality is enabled by downloading separate model checkpoints.

Repo

AI engineering manifesto (December 2025)

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Repo

Show HN: Two physics-based programming languages (WPE/TME and Crystalline)

This project introduces two novel languages, WPE/TME and Crystalline, built on a unified geometric foundation using field theory. WPE/TME is a notation for structural and temporal reasoning that can provide explicit reasoning structures for LLMs. Crystalline is a language for deterministic code synthesis that treats program structure as a geometric field and uses physics-guided evolutionary optimization. Both aim to create explainable and reproducible systems by explicitly encoding structure, hierarchy, and coupling through a shared 4-parameter geometric model.

Repo