Techyon

RD-AGENT 第三讲 · LLM 写的因子长什么样：AI 的研究品味

The third lecture of RD‑AGENT examines LLM‑generated financial factors, noting that code bugs are instantly apparent while flawed assumptions can still produce plausible backtest numbers that rank highly. Using real A‑sh…

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Gemini 4 Argon begins rolling out in Gemini app and Antigravity, but only if you pay

Gemini 4 Argon is beginning to roll out in the Gemini app and Antigravity, but access is limited to paying users. Developers can obtain a 50 % discount on API rates through November.

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Why AnythingLLM is moving from Ollama to Lemonade and llama.cpp

AnythingLLM is shifting from Ollama to the Lemonade embedded SDK combined with llama.cpp for local LLM deployment. This change leverages Lemonade's lightweight integration and llama.cpp's efficient inference to improve p…

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Google wants to host your personal vibe-coded apps in the Play Store

Google is expanding its Play Store AI capabilities by turning the Ask Play chatbot into a full‑featured search engine that can generate detailed app and game recommendations, including side‑by‑side comparison charts, wit…

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Got 2x DGX Sparks. Excited and confused!

The user acquired two Nvidia DGX Spark systems at roughly $6,100 each, citing learning and coding workflow improvements as justification. They noted a lack of discussion about GLM 5.3 flash recipes in the LocalLLaMA subr…

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"New AI fake text generator may be too dangerous to release, say creators" (2019 Guardian headline)

In 2019, OpenAI announced that its GPT‑2 model could generate text so convincing that the risk of malicious use outweighed the benefits of immediate public release, leading the organization to withhold the full model whi…

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GLM-5.3-Flash pruned to half the experts for coding: Q3 (71 GB) solves every task the full 341 GB model solves, IQ2_XXS fits 24 GB VRAM + 32 GB RAM

GLM-5.3-Flash was pruned from 288 to 144 routed experts per layer and quantized using an imatrix built from agentic coding sessions, producing a coder‑specialized model. The Q3‑quantized version (71 GB) matches the full …

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Enabling Token-wise Importance Sampling in Off-Policy RLVR with Proximal KL Regularization

The paper studies off‑policy reinforcement learning with verifiable rewards (RLVR) where data arise from asynchronous generation, rollout‑training mismatch, and response reuse. It shows that token‑wise importance weighti…

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Can an Agent Learn to Predict Its Own Failure? Outcome-Grounded Confidence Calibration in LLM Agents

The paper proposes a confidence‑calibration technique for LLM‑based agents that aligns the model’s self‑reported verbalized confidence with the eventual success or failure of a multi‑step episode rather than with the cor…

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FlashVSR+: Efficient Video Super-Resolution via Hybrid Attention and Reward-Yield Distribution Matching

FlashVSR+ introduces a hybrid attention mechanism that combines strong local correlation from pretrained VSR models with sparse global interaction, achieving up to twice the speed of DiT and 45% lower peak memory, which …

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Spectral-Driven Adaptive-Rank Optimizer for Large Language Model Reinforcement Learning with Verifiable Rewards

Zhipeng Chen, Wayne Xin Zhao, and Ji‑Rong Wen introduced Ares (Adaptive Rank‑Effective Spectral Optimization), an optimizer that dynamically adjusts the rank of gradient updates based on the spectral energy distribution …

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Anyone feel like reverse-engineering CUDA?

A Reddit user on r/LocalLLaMA expresses interest in using AI to reverse‑engineer NVIDIA's CUDA platform and create an open‑source implementation, motivated by dissatisfaction with NVIDIA and the irony of using CUDA cores…

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