Excited to share that TwinRouterBench has been accepted to the #RLEval# Workshop at #CAIS2026# 🎉 As LLM apps become long-horizon agents, one request can trigger many model calls across planning, tool use, retrieval, coding, and verification. That makes per-step LLM routing a core infrastructure problem: sending each call to the cheapest sufficient model without breaking downstream success. TwinRouterBench introduces: ⚡ Static track: 970 router-visible prefixes from 520 instances across SWE-bench, BFCL, mtRAG, QMSum, and PinchBench 🚀 Dynamic track: live SWE-bench Verified evaluation with official task resolution + realized API spend Key result: a router trained on static labels achieves comparable SWE-bench resolve rate while cutting API cost by ~53% vs. an unrouted Opus 4.6 baseline. Paper: Code: Dataset: Website: #LLM# #AgenticAI# #LLMRouting# #Benchmark# #SWEBench#

SK Securities: Memory Revaluation Stemming from a Shift in the Perception of Compute Perception of compute: from consumable to asset AI compute is not a consumable. Rather than a technology good whose value erodes quickly, it could become an infrastructure asset that is redeployed across generations and generates recurring cash flows. From this perspective, AI CapEx is not simply a recurring cost but a process of accumulating a compute stock that can be monetized multiple times going forward. CoreWeave's recent earnings release shows this possibility. Even A100 based resources, six years past launch, are being signed into long term contracts running through 2029, and prices for older GPUs are also holding at meaningful levels. This does not have to be read purely as a supply shortage. Customers choose the cost relative to the performance their workload requires, not the fastest GPU. The newest GPUs handle frontier training and high performance inference, while existing H100/A100 can shift to general inference, fine tuning, batch workloads and the like. This is the concept of compute tiering, analogous to memory tiering. Once the perception of compute changes, the lens through which the expansion of AI infrastructure finance is viewed also changes. If AI infrastructure such as GPUs can generate revenue repeatedly through recontracting after the initial contract ends, it becomes a financeable infrastructure asset rather than simply a piece of electronic equipment. BEP within three years, and the period after that may be the real profit zone The same logic can apply to hyperscaler FCF. Amazon stated that its investments in servers and networking equipment reach BEP on average in less than three years. This means that if the economic life of compute assets including GPUs extends beyond three years, the subsequent period of use may be not simple principal recovery but a zone that generates additional profit. Revenue from new GPUs and second life revenue from the existing compute stock would occur at the same time. If, as with the A100, assets remain applicable into their ninth year, it means a considerable, highly profitable excess return zone opens up between the payback period the market worries about and the actual economic life. The change in memory's status is a structural phenomenon. A change that has to be accepted Once the perception of AI CapEx changes, it is entirely natural that memory's status changes. AI is an asset heavy industry, and physical supply capacity constraints in the real world are not something that can be changed. The reason five year LTAs with high binding force have been signed also becomes clear. Memory has a material impact on the customer's "true" profit zone. If memory does not make money, meeting demand will remain out of reach, volumes will not be secured while memory prices alone rise, and the future revenue curve will be revised downward. Memory making a lot of money is structural, and it is a change that has to be accepted. Inflection point in the change of memory valuation metrics: shareholder returns strengthen in earnest through greater earnings durability and visibility SK Securities maintains its Overweight view on memory. We focus on memory's structurally enhanced earnings power and on the shareholder returns that will begin in earnest, driven by greater durability based on a long cycle. Combined estimated operating profit for Samsung Electronics and SK Hynix is KRW 641tn in 2026 and KRW 977tn in 2027, among the highest globally, and aggressive shareholder returns are expected to be the key foundation for building confidence in the long cycle and for memory's revaluation. We are wary of peak out logic based on the inevitable slowdown in the rate of memory price increases. This is now an era in which the strength of the cycle is absorbed not only through "price" but also through "durability and visibility."

This market has made me reevaluate and consolidate my portfolio into higher conviction names. For me, my core is: $RKLB $NVDA $NBIS $META $ASTS $SOFI $OUST And I dabble with holding other stocks as I research to build more conviction (or cut bait). Been looking for other names for a while now, and I honestly just don't see much opportunity. The AI trade is always interesting but so many names have run. I love other space/tech names but I'm already pretty over indexed. Sometimes doing nothing at all is the best move, while you wait to get hit over the head with a new opportunity. Now I wait.

Every run of exploits forces investment committees to reevaluate the risk-reward. This time its different due to AI. True DeFi TVL is lowest since 2024 Also true (?) Confidence in security (risk) lowest since 2024 DeFi vs TradFi Yield (reward) lowest since 2024 More from @sandmark_news: