Daily Issue
Vol. I — No. 7
04 · 05
Monday, 4 May 2026
Generated 2026-05-04 11:24
google/gemini-2.5-flash-lite-preview-09-2025
愿风指引着你的道路，愿你的刀刃永远锋利。 — 魔兽世界 44 items · 4 sections
§ 0

The Morning

Local weather 1
This morning in
London
Partly cloudy
Today's range
18.2°↓12.9°
currently 16.6°
Feels
15.1°
Rain
39%
Wind
8 km/h
Humid
60%
Rise
05:26
Set
20:28
§ I

US Stocks

Pre-market signal radar 12
US pre-market radar
premarket 2026-05-04
0 Bullish
0 Bearish
12 Neutral
Sector Tape
Servers and Thermal Management 2 names
64 Top: VRT · Neutral · RS -1.5% Bullish 0 / Bearish 0 / 5d -0.6%
Foundry 2 names
63 Top: INTC · Neutral · RS +9.0% Bullish 0 / Bearish 0 / 5d +9.8%
Manufacturing 4 names
63 Top: SANM · Neutral · RS +3.7% Bullish 0 / Bearish 0 / 5d +4.2%
Networking Equipment 4 names
62 Top: CIEN · Neutral · RS -3.5% Bullish 0 / Bearish 0 / 5d -2.5%
Hyperscale Cloud 4 names
58 Top: GOOGL · Neutral · RS +1.2% Bullish 0 / Bearish 0 / 5d +2.6%
Battery and Energy Storage 3 names
54 Top: EOSE · Neutral · RS -13.3% Bullish 0 / Bearish 0 / 5d -10.5%
Compute Mining 4 names
53 Top: WULF · Neutral · RS +0.5% Bullish 0 / Bearish 0 / 5d -2.4%
Energy Infrastructure 1 names
51 Top: VST · Neutral · RS -7.7% Bullish 0 / Bearish 0 / 5d -5.5%
Ticker Setup Move Score Evidence Quality
CLS Celestica Manufacturing
Neutral News watch Low confidence
+0.3% $420.00 5d +2.1%
61 sector flat RS +1.6%

Watchlist item from 3 recent headline(s).

Why Celestica Stock Is Plummeting Today - AOL.com Needs fresh price/news confirmation before becoming an actionable setup.
quote: delayed fallback news: fresh financials: fresh news: 3
AMZN Amazon Hyperscale Cloud
Neutral Sector tailwind Low confidence
-0.0% $268.20 5d +1.6%
60 sector positive RS +0.2%

Watchlist item from positive sector tape, 3 recent headline(s).

AMZN Stock Quote Price and Forecast - CNN Needs fresh price/news confirmation before becoming an actionable setup.
quote: delayed fallback news: fresh financials: fresh news: 3
quotes: nasdaq 24 24/24news: google_news_rss 23 23/24filings: sec 24 24/24, fallback 24

Generated from public market data and news for research and education. Not financial advice; data may be delayed, incomplete, or wrong.

§ II

From the arXiv

arXiv preprints 10 of 20
cs.AIarxiv:2605.00505v1Lead article

LLM-Oriented Information Retrieval: A Denoising-First Perspective

Lu Dai, Liang Sun, Fanpu Cao, Ziyang Rao, Cehao Yang

his paper argues that the shift to LLM-centric information retrieval (IR) makes noise a critical bottleneck, causing hallucinations and reasoning failures due to limited LLM attention. The core contribution is conceptualizing this paradigm shift through a four-stage framework of IR challenges (inaccessible to unverifiable) and providing a comprehensive taxonomy of signal-to-noise optimization techniques across the entire IR pipeline.

Read abstract →Full PDFLINNET · SCORE9RELEVANCE
Figure 1. Challenge shifts in the history of IR.
Figure 1. Challenge shifts in the history of IR.
cs.AIarxiv:2605.00742v1

Position: agentic AI orchestration should be Bayes-consistent

Theodore Papamarkou, Pierre Alquier et al.

This paper argues that while making Large Language Models (LLMs) themselves explicitly Bayesian is difficult, the **orchestration layer** of agentic AI systems should adopt **Bayesian Decision Theory (BDT)**. This provides a principled framework for managing u…

abstractpdfLINNET · SCORE9RELEVANCE
cs.AIarxiv:2605.00528v1

SAGA: Workflow-Atomic Scheduling for AI Agent Inference on GPU Clusters

Dongxin Guo, Jikun Wu et al.

SAGA addresses the inefficiency of scheduling independent LLM calls for AI agent workflows on GPU clusters by shifting to **program-level scheduling**. It treats the entire agent workflow as the first-class schedulable unit, using Agent Execution Graphs to pre…

abstractpdfLINNET · SCORE9RELEVANCE
cs.AIarxiv:2605.00519v1

Silicon Showdown: Performance, Efficiency, and Ecosystem Barriers in Consumer-Grade LLM Inference

Allan Kazakov, Abdurrahman Javat

This paper systematically analyzes the performance and efficiency trade-offs for running large LLMs (70B+ parameters) on consumer hardware, comparing Nvidia and Apple Silicon. It identifies a "Backend Dichotomy" on Nvidia, where the new NVFP4 format boosts thr…

abstractpdfLINNET · SCORE9RELEVANCE
cs.AIarxiv:2605.00737v1

To Call or Not to Call: A Framework to Assess and Optimize LLM Tool Calling

Qinyuan Wu, Soumi Das et al.

This paper introduces a principled framework, inspired by decision-making theory, to assess and optimize when Large Language Models (LLMs) should use external tools, focusing specifically on web search. The framework evaluates tool-use decisions based on neces…

abstractpdfLINNET · SCORE9RELEVANCE
Given input x x , the model ℳ \( \mathcal{M} \) decides π ​ ( x ) ∈ { 0 , 1 } \( \pi \)(x)\( \in \)\{0,1\} to call a tool (response r r ) or not, producing y = ℳ ​ ( x , r ) y=\( \mathcal{M} \)(x,r) or y = ℳ ​ ( x ) y=\( \mathcal{M} \)(x) . We compare NO TOOL, ALWAYS TOOL, and SELF-DECISION, and evaluate decisions via need (requires help), utility (performance gain), and affordability (cost vs. gain), distinguishing perceived vs. true quantities.
Given input x x , the model ℳ \( \mathcal{M} \) decides π ​ ( x ) ∈ { 0 , 1 } \( \pi \)(x)\( \in \)\{0,1\} to call a tool (response r r ) or not, producing y = ℳ ​ ( x , r ) y=\( \mathcal{M} \)(x,r) o…
№06
cs.LG
9

Evaluating the Architectural Reasoning Capabilities of LLM Provers via the Obfuscated Natural Number Game

Lixing Li

This paper introduces the Obfuscated Natural Number Game to evaluate LLMs' **Architectural Reasoning**, defined as synthesizing proofs using only local axioms in an unfamiliar doma…

abstractpdf
№07
cs.LG
9

RunAgent: Interpreting Natural-Language Plans with Constraint-Guided Execution

Arunabh Srivastava, Mohammad A. et al.

RunAgent is a multi-agent platform designed to reliably execute natural-language plans by enforcing stepwise execution through constraints and rubrics. It translates flexible natur…

abstractpdf
№08
cs.LG
9

Stable-GFlowNet: Toward Diverse and Robust LLM Red-Teaming via Contrastive Trajectory Balance

Minchan Kwon, Sunghyun Baek et al.

This paper introduces **Stable-GFlowNet (S-GFN)** to improve the stability and diversity of LLM red-teaming using Generative Flow Networks (GFNs). S-GFN achieves stability by elimi…

abstractpdf
№09
cs.CL
9

AGoQ: Activation and Gradient Quantization for Memory-Efficient Distributed Training of LLMs

Wenxiang Lin, Juntao Huang et al.

AGoQ introduces a novel quantization scheme for memory-efficient LLM training by employing layer-aware quantization for near 4-bit activations and precision-preserving 8-bit quanti…

abstractpdf
№10
cs.CL
9

Beyond Benchmarks: MathArena as an Evaluation Platform for Mathematics with LLMs

Jasper Dekoninck, Nikola Jovanović et al.

This paper introduces **MathArena** as a continuously maintained evaluation platform designed to overcome the limitations of static benchmarks for assessing LLM mathematical reason…

abstractpdf
See all 20 papers →
§ III

The Town Square

Hacker News 3
369
pts
Top story
Let's Buy Spirit Air

The provided URL is a website titled "Let's Buy Spirit Air," suggesting a campaign or proposal related to the acquisition of Spirit Airlines.

letsbuyspiritair.com3 May discuss on HN →
218
For thirty years I programmed with Phish on, every day
christophermeiklejohn.com3 May
124
LLMs Are Not a Higher Level of Abstraction
lelanthran.com3 May
compiled overnight by google/gemini-2.5-flash-lite-preview-09-2025 · end of issue no. 7 · thank you for reading