💬 Prompt Engineering - all666666all

⚙️AutoML Academic

arxiv.org·

shoo99/paper-rag: A private, fully-local RAG over your own PDFs: BGE-M3 + embedded Qdrant + a local LLM via Ollama. ~150 lines, nothing leaves your machine.

🔍Vector Databases Code

github.com··DEV

TAHOE: Text-to-SQL with Automated Hint Optimization from Experience

🔍Vector Databases Academic

arxiv.org·

Which is faster: Gemini 3.5 Flash or Kimi K2.6 on Cerebras

🧠Symbolic AI Blog

cerebras.ai·

Evaluating Advanced Prompting on Gemini Flash for Multi-Hop Biomedical QA

🧠Symbolic AI Academic

arxiv.org·

The inference API that learns from your failures (Sponsor)

🧬AGI Self-Evolution Blog

pioneer.ai·

Your ML Model Is Only as Good as the Dataset You Give It. Most Organisations Give It the Wrong One.

⚙️AutoML Blog

medium.com·

How Far Can Prompting Go for Minimal-Edit Ukrainian Grammatical Error Correction?

🧠Symbolic AI Academic

arxiv.org·

KiloBench - Because Your Benchmark Score Doesn't Pay the Bill

📦Model Compression News Blog

blog.kilo.ai·

DD-GEPA: Prompt Optimization for Dialogue Disentanglement Focusing on Task Instruction and Utterance Representation

🧠Symbolic AI Academic

arxiv.org·

The Periodic Table of LLM Reasoning: A Structured Survey of Reasoning Paradigms, Methods, and Failure Modes

🧠Symbolic AI Academic

arxiv.org·

Contextual Identity Laundering: How Claude’s Image Refusal Can Be Routed Through Web Search

🔍Vector Databases

lesswrong.com·

Less-relevant results

dots.tts首包54ms，SWE自进化破50%

📦Model Compression

ai-brief.liziran.com·

In-Context Learning for the Imputation of Public Opinion Data with Large Language Models

📦Model Compression Academic

arxiv.org·

The Structural Attention Tax: How Retrieval Format Hijacks In-Context Learning Independent of Content

🧠Symbolic AI Academic

arxiv.org·

Quo Vadis, Visual In-Context Learning? A Unified Benchmark Across Domains and Tasks

⚙️AutoML Academic

arxiv.org·

Enhancing LLM-Based Code Translation with Verified Multi-Semantic Representations

🧠Symbolic AI Academic

arxiv.org·

Phase Transitions in Attention: A Bayesian Theory of Copy Head Emergence

🧬AGI Self-Evolution Academic

arxiv.org·

The Shibboleth Effect: Auditing the Cross-Lingual Distributional Skew of Large Language Models

🔍Vector Databases Academic

arxiv.org·

Failing to Ragebait the New Gemma

Beyond Model Size: Probing the Gaps in Visual in-Context Learning by Training a Tiny Model

shoo99/paper-rag: A private, fully-local RAG over your own PDFs: BGE-M3 + embedded Qdrant + a local LLM via Ollama. ~150 lines, nothing leaves your machine.

TAHOE: Text-to-SQL with Automated Hint Optimization from Experience

Which is faster: Gemini 3.5 Flash or Kimi K2.6 on Cerebras

Evaluating Advanced Prompting on Gemini Flash for Multi-Hop Biomedical QA

The inference API that learns from your failures (Sponsor)

Your ML Model Is Only as Good as the Dataset You Give It. Most Organisations Give It the Wrong One.

How Far Can Prompting Go for Minimal-Edit Ukrainian Grammatical Error Correction?

KiloBench - Because Your Benchmark Score Doesn't Pay the Bill

DD-GEPA: Prompt Optimization for Dialogue Disentanglement Focusing on Task Instruction and Utterance Representation

The Periodic Table of LLM Reasoning: A Structured Survey of Reasoning Paradigms, Methods, and Failure Modes

Contextual Identity Laundering: How Claude’s Image Refusal Can Be Routed Through Web Search

dots.tts首包54ms，SWE自进化破50%

In-Context Learning for the Imputation of Public Opinion Data with Large Language Models

The Structural Attention Tax: How Retrieval Format Hijacks In-Context Learning Independent of Content

Quo Vadis, Visual In-Context Learning? A Unified Benchmark Across Domains and Tasks

Enhancing LLM-Based Code Translation with Verified Multi-Semantic Representations

Phase Transitions in Attention: A Bayesian Theory of Copy Head Emergence

The Shibboleth Effect: Auditing the Cross-Lingual Distributional Skew of Large Language Models