https://largevia.s34r.top

largeapp下载经典版

large今年已70岁高龄,KBC台海外总编辑,记者阮方雄这次是第猪上树三次加入长沙过访,他仍浸沉在越南海岛主权问题上。到达长沙时,她亲眼见证了各岛军民的生计,她感动地说:我们侨胞此访已深切了然,长沙已经并始终是越南的一局部血肉。裴本和阮氏秋夫妇拍摄了好些影像和录像,记录情节各岛的行程 ߓ�,准备回到新喀里多尼亚时没眼人做...

ayrc.net 2025年10月20日

Human-Inspired Learning for Large Language Models via Obvious Record and Maximum-Entropy Method Discovery

Large Language Models (LLMs) have achieved substantial progress across a wide range of reasoning, generation, and problem-solving tasks[chang2024survey]. Their training paradigm—predicting the next token over massive corpora—enables them to capture broad statistical regularities and to perform well on problems that are commonly repre...

arxiv.org

[2604.11556] FM-Agent: Scaling Formal Methods to Large Systems via LLM-Based Hoare-Style Reasoning

Haoran DingZhaoguo WangHaibo ChenView a PDF of the paper titled FM-Agent: Scaling Formal Methods to Large Systems via LLM-Based Hoare-Style Reasoning, by Ha... the first framework that realizes automated compositional reasoning for large-scale systems. Leveraging LLMs, FM-Agent introduces a top-down paradigm to automatically g...

arxiv.org 4月13日

Towards Solving the Gilbert-Pollak Conjecture via Large Language Models

developed LeanDojo, an open environment enabling large-scale training of these systems. Most recently, Goedel-Prover(Lin et al., )was introduced as a frontier model, achieving top performance among open-weight models on the MiniF2F benchmark. AI for Scientific Discovery.Artificial Intelligence has catalyzed paradigm shifts across various scie...

arxiv.org 2025年12月26日

[2606.26774] End-to-end probabilistic hierarchical forecasting of large hierarchies via probabilistic top-down

Lorenzo ZambonDario AzzimontiGiorgio CoraniView a PDF of the paper titled End-to-end probabilistic hierarchical forecasting of large hierarchies via probabilistic top-down, by Lorenzo Zambon and 2 other authors View PDFHTML (experimental)Abstract:Retail and supply chain operations rely on demand forecasts to drive decisions, from replen...

arxiv.org 6月25日

The Navigation Paradox in Large-Context Agentic Coding: Graph-Structured Dependency Navigation Outperforms Retrieval in Architecture-Heavy TasksCode, ...

description. Example: Change the error message ‘incorrect email or password’ to ‘invalid credentials’ — a BM25 query surfacesapp/resources/strings.pyas the top re... •G3 — Hidden (tasks 21–30):Required files share no semantic overlap with the task description and are reachable only via structural graph traversal. Example: Add alogge...

arxiv.org 2月15日

Joint Selection for Large-Scale Pre-Training Data via Policy Gradient-based Mask Learning

we also conducted an experiment in a more semantically duplicated situation, and the model pre-trained on top 20% high-quality samples exhibited worse final performance compared to the raw dataset after long-term pre-training. In both cases, our joint learning approach shows the best performance.

arxiv.org 2025年12月30日

[2503.01839] Jailbreaking Safeguarded Text-to-Image Models via Large Language Models

Abstract page for arXiv paper 2503.01839: Jailbreaking Safeguarded Text-to-Image Models via Large Language Models Computer Science Cryptography and Security arX... safety filters are often added on top of text-to-image models, or the models themselves are aligned to reduce harmful outputs. However, these defenses remain vulnerable wh...

arxiv.org 2025年3月3日

Large Language Models for Sequential Decision-Making: Improving In-Context Learning via Supervised Fine-Tuning

Q^(xq∣P)=xq⊤Γ−1(1M∑i=1Myixi).\widehat{Q}(x_{\mathrm{q}}\mid P)=x_{\mathrm{q}}^{\top}\Gamma^{-1}\left(\frac{1}{M}\sum_{i=1}^{M}y_{i}x_{i}\right). (5) Moreover, in the noiseless realizable case, εQ(M,N)≜ݔ�w∗,xi,xq[(Q^(xq∣P)−⟨w∗,xq⟩)2]≤(d+1)tr⁡(Λ)M+(1+2d+d2κ)tr⁡(Λ)N2,\varepsilon_{Q}(M,N)\tr...

arxiv.org 2012年12月12日

CodeLutra: Boosting LLM Code Generation via Preference-Guided Refinement

comparative data from both successful and failed code attempts, enabling low-performing models to rival top-tier solutions without external datasets or ultra-large LLMs’ fee... attempts and likelihood regularization forCodeLutra, and revealCodeLutraimproves the performance via reducing syntax errors and improving incorrect answers across iterat...

arxiv.org 2024年11月15日

没有更多结果了~

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