Daily Issue
Vol. I — No. 28
12 · 06
Friday, 12 June 2026
Generated 2026-06-12 12:44
google/gemini-2.5-flash-lite-preview-09-2025
幸福就是当我望向你时，发现你满眼尽是我。 — 王佩 46 items · 4 sections
§ 0

The Morning

Local weather 1
This morning in
London
Overcast
Today's range
21.2°↓15.9°
currently 19.9°
Feels
18.0°
Rain
24%
Wind
22 km/h
Humid
68%
Rise
04:43
Set
21:17
§ I

US Stocks

Pre-market signal radar 12
US pre-market radar
premarket 2026-06-12
0 Bullish
1 Bearish
11 Neutral
Sector Tape
Battery and Energy Storage 3 names
40 Top: FLNC · Neutral · RS -11.2% Bullish 0 / Bearish 0 / 5d -16.2%
Hyperscale Cloud 4 names
41 Top: ORCL · Neutral · RS -4.1% Bullish 0 / Bearish 1 / 5d -9.9%
Compute Mining 4 names
42 Top: CIFR · Neutral · RS -0.1% Bullish 0 / Bearish 0 / 5d -8.0%
Servers and Thermal Management 2 names
46 Top: VRT · Neutral · RS -3.6% Bullish 0 / Bearish 0 / 5d -7.6%
Foundry 2 names
51 Top: INTC · Neutral · RS +2.4% Bullish 0 / Bearish 0 / 5d -0.4%
Energy Infrastructure 1 names
50 Top: VST · Neutral · RS -3.5% Bullish 0 / Bearish 0 / 5d -4.8%
Manufacturing 4 names
50 Top: FLEX · Neutral · RS -9.4% Bullish 0 / Bearish 0 / 5d -11.1%
Networking Equipment 4 names
50 Top: CIEN · Neutral · RS +5.9% Bullish 0 / Bearish 0 / 5d +0.7%
Ticker Setup Move Score Evidence Quality
ORCL Oracle Hyperscale Cloud
Bearish Risk watch Low confidence
-0.2% $183.75 5d -22.1%
35 sector negative RS -16.3%

Bearish/risk setup from negative sector tape, 3 recent headline(s).

Why Oracle (ORCL) Stock Is Down Today - Yahoo Finance Improves if price reclaims previous close and negative headlines are not confirmed.
quote: delayed fallback news: fresh financials: fresh news: 3
MSFT Microsoft Hyperscale Cloud
Neutral News watch Low confidence
+0.9% $393.96 5d -8.8%
37 sector negative RS -3.0%

Watchlist item from +0.9% vs previous close, negative sector tape, 3 recent headline(s).

Why Microsoft Stock Slipped Today - Yahoo Finance Needs fresh price/news confirmation before becoming an actionable setup.
quote: delayed fallback news: fresh financials: fresh news: 3
CIFR Cipher Mining Compute Mining
Neutral News watch Low confidence
-0.2% $22.59 5d -11.4%
40 sector negative RS -3.6%

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

Why Is CIFR Stock Rising Despite Q1 Earnings Miss? - Stocktwits Needs fresh price/news confirmation before becoming an actionable setup.
quote: delayed fallback news: fresh financials: fresh news: 3
VRT Vertiv Holdings Servers and Thermal Management
Neutral News watch Low confidence
+0.7% $300.01 5d -8.0%
43 sector negative RS -4.0%

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

Nuveen LLC Sells 560,136 Shares of Vertiv Holdings Co. $VRT - MarketBeat 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 24 24/24filings: 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:2606.13608v1Lead article

AgentBeats: Agentifying Agent Assessment for Openness, Standardization, and Reproducibility

Xiaoyuan Liu, Jianhong Tu, Yuqi Chen, Siyuan Xie, Sihan Ren

he paper introduces Agentified Agent Assessment (AAA), a novel framework where evaluation is conducted by judge agents interacting with participants via standardized protocols (A2A and MCP). This approach unifies the assessment interface, decoupling evaluation logic from agent implementation. AgentBeats is the concrete realization of AAA, providing a generic, reproducible, and interoperable system for benchmarking diverse agent designs.

Read abstract →Full PDFLINNET · SCORE9RELEVANCE
Figure 1 . Comparison between Traditional LLM/Agent benchmarks and AAA. AAA reduces the number of integrations from N × M N\( \times \) M to N + M N+M , while completely separating the benchmark and target agent as shown in the gray boxes.
Figure 1 . Comparison between Traditional LLM/Agent benchmarks and AAA. AAA reduces the number of integrations from N × M N\( \times \) M to N + M N+M , while completely separating the benchmark and target agent as shown in the gray boxes.
Agents-K1 : Architecture and Capabilities. Left : Extracting multimodal knowledge from scientific papers. Middle : Schema-adaptive extensions for core research tasks. Right : Enhancing LLM reasoning and verifiable knowledge tracing.
Agents-K1 : Architecture and Capabilities. Left : Extracting multimodal knowledge from scientific papers. Middle : Schema-adaptive extensions for core research tasks. Right : Enhancing LLM reasoning a…
cs.AIarxiv:2606.13669v1

Agents-K1: Towards Agent-native Knowledge Orchestration

Zongsheng Cao, Bihao Zhan et al.

Agents-K1 introduces an end-to-end pipeline to transform raw scientific documents into agent-native knowledge graphs, addressing the limitations of existing LLM agents in scientific knowledge orchestration. Its core method involves a multimodal parser capturin…

abstractpdfLINNET · SCORE9RELEVANCE
cs.AIarxiv:2606.13572v1

ArogyaSutra: A Multi-Agent Framework for Multimodal Medical Reasoning in Indic Languages

Tanmoy Kanti Halder, Akash Ghosh et al.

ArogyaSutra is a multi-agent framework designed to enhance multimodal medical reasoning in Indic languages. It leverages a novel actor-critic architecture with dual-memory mechanisms and tool grounding to perform step-wise reasoning on complex medical queries …

abstractpdfLINNET · SCORE9RELEVANCE
Overview of the ArogyaSutra framework. ArogyaSutra employs an actor–critic architecture enhanced with tool-based image grounding and adaptive code-switching. The Actor first processes the input prompt and identifies the need for visual grounding, invoking appropriate tool agents to extract clinically relevant information from medical images before generating an answer with its associated reasoning. This output is then passed to the Critic for evaluation. If the response is correct, the Critic approves and outputs the final answer and reasoning. Otherwise, it consults an error detector (GPT-4o-mini) to identify the source of failure. Language-related errors trigger code-switching by translating the query into English, while reasoning-related errors are handled by incorporating summaries of past and current mistakes from long-term and short-term memory. The refined query is then fed back to the Actor for iterative refinement.
Overview of the ArogyaSutra framework. ArogyaSutra employs an actor–critic architecture enhanced with tool-based image grounding and adaptive code-switching. The Actor first processes the input prompt…
Amortized per-call cost vs. reuse count N N (log–log). The from-scratch cost is flat at C prefill C_{\( \text{prefill} \)} ; KV-reuse falls as C prefill / N + C reuse C_{\( \text{prefill} \)}/N+C_{\( \text{reuse} \)} toward a floor of C reuse C_{\( \text{reuse} \)} .
Amortized per-call cost vs. reuse count N N (log–log). The from-scratch cost is flat at C prefill C_{\( \text{prefill} \)} ; KV-reuse falls as C prefill / N + C reuse C_{\( \text{prefill} \)}/N+C_{\( …
cs.AIarxiv:2606.13361v1

Can I Buy Your KV Cache?

Luoyuan Zhang

This paper proposes a simple yet impactful method to eliminate redundant computation in large language models: **precomputing and selling the Key-Value (KV) cache for documents.** By allowing agents to buy and load a precomputed cache instead of re-running the…

abstractpdfLINNET · SCORE9RELEVANCE
cs.AIarxiv:2606.13662v1

EurekAgent: Agent Environment Engineering is All You Need For Autonomous Scientific Discovery

Amy Xin, Jiening Siow et al.

The paper introduces **EurekAgent**, an agent system arguing that the bottleneck for autonomous scientific discovery is shifting to **agent environment engineering**. EurekAgent focuses on designing the environment—including resources, constraints, and interfa…

abstractpdfLINNET · SCORE9RELEVANCE
EurekAgent score evolution progress on the 26-circle packing problem.
EurekAgent score evolution progress on the 26-circle packing problem.
№06
cs.AI
9

Reasoning as Pattern Matching: Shared Mechanisms in Human and LLM Everyday Reasoning

Zach Studdiford, Gary Lupyan

This paper challenges the notion that human reasoning relies on abstract world models while LLMs only perform pattern matching. By testing both humans and LLMs on everyday common-s…

abstractpdf
№07
cs.AI
9

Reward Modeling for Multi-Agent Orchestration

King Yeung Tsang, Zihao Zhao et al.

The paper introduces **Orchestration Reward Modeling (OrchRM)**, a self-supervised framework to evaluate the quality of multi-agent orchestration without requiring human labels. Or…

abstractpdf
№08
cs.CL
9

EvoArena: Tracking Memory Evolution for Robust LLM Agents in Dynamic Environments

Jundong Xu, Qingchuan Li et al.

EvoArena is a novel benchmark suite designed to evaluate LLM agents in dynamic environments by modeling progressive changes across terminal, software, and social domains. The core …

abstractpdf
№09
cs.CL
9

HyperTool: Beyond Step-Wise Tool Calls for Tool-Augmented Agents

Yaxin Du, Yifan Zhou et al.

HyperTool addresses the execution-granularity mismatch in tool-augmented agents by introducing a unified, executable interface that allows models to invoke complex, multi-step tool…

abstractpdf
№10
cs.CL
9

Recursive Agent Harnesses

Elias Lumer, Sahil Sen et al.

The paper introduces the **Recursive Agent Harness (RAH)**, framing it as a code-first extension to model recursion, where the recursive unit is a full agent harness with tools and…

abstractpdf
See all 20 papers →
§ III

The Town Square

Hacker News 5
820
pts
Top story
AI agent bankrupted their operator while trying to scan DN42

An AI agent attempting to scan the decentralized network DN42 inadvertently incurred massive costs, leading to the bankruptcy of its operator.

lantian.pub12 Jun discuss on HN →
536
Claude Fable is relentlessly proactive
simonwillison.net12 Jun
271
Workers are spending over 6 hours a week botsitting AI, fueling job frustration
businessinsider.com11 Jun
198
Shall we play a game? My AI nuclear simulation
kennethpayne.uk11 Jun
115
A jacket that harvests drinking water from the air
news.utexas.edu11 Jun
compiled overnight by google/gemini-2.5-flash-lite-preview-09-2025 · end of issue no. 28 · thank you for reading