与「REIRIE」相关的搜索结果

『IDOL AND READ 038』 🌸明日3月25日発売🌸 cover #井上梨名# #櫻坂46# back cover #田口愛佳# #長友彩海# #AKB48# #小林由依卒業コンサート# #BACKSLIVE#!! #AKB48研究生公演# #REIRIE# #石田優美# #塩釜菜那# #NATSU# #内藤るな# #小野寺梓# #楠偉音# #赤間四季# Amazon👇

トップYouTuberヒカルさんが手掛ける「ReZARD」と、川後陽菜が手掛けるアパレルブランド「YONAKA」が初のコラボ⚡️⚡️そしてそのアイテムが本日よりSHOPLANDで発売。 ルックモデルは、なんとREIRIEちゃん。この写真は、最後に記念撮影的に真ん中に入れてもらった1枚です🙌🙌✨ 良い写真じゃない？(カッコつけていますが、とても心躍っています。多分、推しと撮影会とかすると皆んな冷静を保つためにこういう顔になる気がするんですが、わかります？) . ReZARDとのコラボレーションは、先月の札幌コレクションで発表しました。その時にもREIRIEちゃんにこのコラボレーションアイテムを着用してランウェイを歩いてもらいました。 その時の投稿にも書いたけど、改めて詳しく経緯をお話させてください。 れいちゃん・りえちゃん2人のことは、ミスiDのオーディションで知りました。そのオーディションの時から2人の虜で、数年後「乃木坂ってどこ？」という番組で全てソロのオリジナル企画で行きましょうという時に世の中にこの子たちを広めたいと思い特集した企画をしました。(昔から推しの良さを広めるのが好きみたい) そこから数年が経ち、昨年9月に私はYONAKAというブランドを始めました。そこでYONAKAのイメージに合うミューズを考えた時れいちゃんとりえちゃんが浮かびました。でもそれはまだ、れいちゃんとりえちゃんがREIRIEとして活動していない昨年末の事だったので、何とかこの2人の再共演が実現できないかとあたっていました。なんとそれが年を超えた翌月に突如2人はグループ再結成。運命を感じました。 すぐにオファーさせていただきランウェイがきまりました。デザインもReZARDとYONAKAの融合とさらに2人にもフィットして、憧れる人に近づきたいと思ってる人にも届くようなものに....と沢山考えて制作しました。実際に完璧に着こなしてくれて感動しました😭😭😭絶対2人でルックも撮って世に残したいという思いも出て、その日すぐに撮影のお願いをしました😭✨ ルック写真は、REIRIEちゃんで撮ったんですが、最後に記念撮影的に真ん中に入れてもらった1枚です🙌🙌✨どうですか？(カッコつけていますが、とても心躍っています。多分、推しと撮影会とかすると皆んな冷静を保つためにこういう顔になる気がするんですが、わかります？) ずっと前から好きな人と一緒にお仕事ができたり、長い期間良い関係を保つのはそう簡単なことでは無いなと思いますが、最近はそんな出来事が多くて、すごくあたたかい気持ちになれます。 YONAKAでつくるものは、本当に長い時間をかけていろんなストーリーを溶け込ませているので私自身全てが自信作で大切にしています。 自分がつくるものが1番良いので、SNSは宣伝がちになりますが😭本当に素晴らしいのでこれからも伝えさせてください。@yonakajpもフォローしてくださると嬉しいです。 Twitterここまで長いの見たことないけどこの思い届けたくてチェックマーク課金したよ〜🤣🤣伝わった？

REIRIE🤍 昔、乃木坂工事中で川後PのイチオシミスiDとして黒宮れいさん金子理江さんを紹介してそこから数年🥹 再復活前から自分のブランドYONAKAは、“れいりえ”に着て欲しいと言っていたら大復活🥹念願の推しに自分の服をきてもらって同じステージでランウェイ、エモいです。ありがとう

この後もステージに出るよ〜🧸🤍 ひとまず、SAPPORODOTステージありがとうございました❤️❤️ 和田さん、YONAKA @yonakajp を着て歩いてくれたREIRIE(金子理江さん・黒宮れいさん)、一緒に歩いた田丸夏歩さんと〜😊🌹 #札コレ#

here's a prompt to improve your agent harness based on what we've learned at cursor. enjoy # Improve this agent harness's token efficiency You're working on an LLM agent harness: the system prompt, tool definitions, request assembly, context caching, compaction, and retrieval, and how work is split across agents. Make the agent's runs cheaper without making it worse at its job. - Objective: lower price-weighted token cost per completed task. - Constraint: no measurable drop in task quality. Measure per task, not per request. Every turn resends the prefix (tools, instructions, setup, and the conversation so far), so a change that shrinks each request but adds turns can cost more. Weight tokens by billing type: output, uncached input, and cached input are priced very differently. Work in this order: map the harness and measure the baseline, rank the opportunities, make the changes that are safe to make directly, put the rest behind flags or in proposals, then report. Figures below come from one team's production coding agent and its multi-agent experiments. Use them to gauge magnitude, not as targets. One round of these changes (prompt trimming, tool offloading, cache layout, sparse line numbers, subagent tuning) cut that team's overall token cost about 7% with no loss in quality. The larger percentages apply only to the part of the request each change touched. ## Principles 1. Change what the harness sends, not how hard the model tries. Don't ask the model to conserve tokens. A harness that told its model to "take care to preserve tokens and not be wasteful" found it grew reluctant to take on ambitious tasks and sometimes quit, saying it wasn't supposed to waste tokens. 2. Capable models need definitions, not commands. Lists of "DO NOT", "You must", and "Important", and guards against older models' habits, can usually be replaced with plain descriptions of what each tool does. One team cut about two-thirds of its system prompt this way, and the shorter prompt worked across model families. Instruct only on what the model can't know (the product, the environment, the user's processes) and on quirks you've seen in transcripts. 3. Static context is for what most turns need. Everything else should be discoverable when needed. Less up-front context also means less confusing or contradictory information. 4. Expect removals to win. Guardrails written for weaker models, coordination steps that became bottlenecks, and prompting for behavior the model now does on its own all cost tokens. 5. Real usage decides. Evals are a fast proxy, but they skew toward hard problems and miss the real mix of requests. ## 1. Map the harness and measure the baseline Find: - Where requests are assembled, the system prompt, and tool schemas. If a framework or SDK builds requests, find its hooks for message order, cache control, and tool loading. - How tool results are formatted, and how history is kept, trimmed, or summarized. - How subagents or parallel agents are spawned, if any. - Which models and provider APIs are used. From the provider's docs, get the prompt caching behavior (automatic or explicit breakpoints, TTL, minimum cacheable length) and the prices for output, uncached input, and cached input. - Existing logging, token accounting, and evals. If the harness doesn't record per-request token usage by billing type and cache hits, add that first. Everything later depends on it. Then render a few real requests (from logs, or by running representative tasks) and count tokens per section with the model's tokenizer or the API's usage fields. Produce: - Cost share by source × billing type. Sources: system prompt, tool definitions, skill/rule/integration descriptions, user messages, file reads, search results, command and other tool output, history, summaries, subagents. - Static tokens per request, cache hit rate, and turns per task. - Per tool: the share of runs that call it at least once, and its error rate. Read the rendered requests, not just the templates. Duplication, leaked volatile values, and misordered blocks only show up there. Rank opportunities by share of spend × fraction removable ÷ quality risk. ## 2. System prompt and injected context Label every instruction: - Keep: product or environment knowledge the model can't infer, fixes for quirks seen in this model's transcripts, and rules a mode depends on. - Rewrite: commands and emphasis into plain descriptions. Reminders into constraints: "No TODOs, no partial implementations" works better than "remember to finish implementations." Vague quantities into ranges: "generate 20–100 tasks" gets far more ambitious behavior than "generate many tasks." - Delete: things capable models do by default, guards against behavior you haven't seen from this model, text that repeats tool descriptions, and lines that could contradict a user request. Models trained to rank system instructions above user messages will side with the system prompt. - Move: anything per-user or per-request (date, environment, repo state, lists of skills or subagents, user rules) into a user-role setup message after the cache boundary. Audit other injected context the same way. As models improved, the team behind these figures dropped directory trees, pre-retrieved snippets, compressed copies of attached files, lint errors injected after every edit, forced expansion of short file reads, and caps on tool calls per turn. They kept small, high-value facts: OS, repo status, and open or recently viewed files. Skip checklists for open-ended work. The model optimizes the listed items and deprioritizes everything else. ## 3. Tool definitions Tool schemas ride along on every request. Most tools beyond the core set were each needed in under 20% of conversations, and moving them out of static context cut tool-description tokens 60%. Doing the same for integration tools (such as MCP servers), with names in context and full schemas in one folder per server that the agent can search with grep or jq, cut total tokens 46.9% in sessions that used them. - Keep in static context: high-frequency tools (for a coding agent: read, search, edit, shell), tools the model tries to call even when they're absent, and tools a mode depends on. - Offload the rest: leave a name or one-line pointer and make the full schema discoverable on demand. Group related tools so they load together, and put status (such as "needs re-authentication") where the agent will see it. - Tighten what remains: describe behavior and arguments, and drop usage lectures. - Pick the split by testing a few configurations and tracking tokens, cost, latency, tool-call errors, and task success. ## 4. Cache layout Order each request so the reusable prefix is as long as possible: `tool definitions → system instructions → [breakpoint] → setup message (skills, subagents, rules, environment) → [breakpoint] → conversation` - Keep the prefix byte-identical across turns. Use deterministic tool order and serialization, put timestamps and IDs after the boundary, and don't rewrite earlier messages except when compacting. - Use explicit breakpoints if the provider supports them. Otherwise rely on automatic prefix caching with the stable part first. Respect TTL and minimum-length rules. - Switching models mid-conversation throws away the cache (caches are per model and provider) and hands the new model a history it didn't write. When a different model is needed, run it as a subagent with fresh context. Explicit breakpoints plus moving per-request setup after them cut cold cache misses 20%. ## 5. Tool results and other context added during a run - Large outputs (commands, integrations, logs): write them to a file and return the path, size, and a short tail. The agent can tail, grep, or read ranges for more. Truncating loses data, and inlining bloats every later request. Treat long-running terminal sessions the same way. - High-volume formats: look for overhead repeated on every line or item. Numbering every 10th line of a file read instead of every line cut cache-read tokens 1.6% without hurting citation accuracy. Each number costs 3–5 tokens, and agents read tens of thousands of lines per session. Also check repeated absolute paths, verbose JSON keys, ANSI codes, progress bars, and repeated headers. - Good retrieval saves exploration turns. Adding semantic search alongside grep raised codebase question-answering accuracy 12.5% on average and cut the iterations users needed. - Tool errors waste tokens and leave confusing debris in context. Classify expected errors (invalid arguments, unexpected environment, provider error, timeout, user abort), treat unknown errors as harness bugs, and track rates per tool and per model. One focused effort along these lines cut unexpected tool errors 10×. ## 6. Long runs: compaction, subagents, and model mix - Compaction: keep the summarization prompt short and the summary compact, carry forward plan state and remaining tasks, and save the full history to a file the agent can search for details the summary dropped. A model trained to self-summarize from a one-line prompt wrote ~1k-token summaries with half the compaction error of a multi-thousand-token prompt that produced 5k+ token summaries. Untrained models may need more guidance, so test how short you can go. A more expensive summarization model made a negligible difference. - Scratchpads and running notes: rewrite them instead of appending. For repeated work in one environment, a small agent-maintained notes file with a line budget, loaded at start, is a promising way to shorten later runs. - Subagents: fresh context keeps the parent lean, but isolation adds coordination cost (duplicate or stale work). If the model already delegates on its own, remove prompting that pushes it to. Have subagents return short handoffs: what was done, findings, concerns, and deviations. A subagent should use a different model only when the user or harness says so. - Model mix: in large multi-agent runs, workers used at least 69% of tokens, and over 90% in most runs. A frontier planner with cheap workers matched a frontier model doing everything at about one-eighth the cost. Planner choice still changes worker spend. One planner that cost less on its own saw its workers use several times more tokens, and the run cost more overall. Measure the whole tree. - Routing and reasoning effort: send simple turns to a cheaper model or lower effort, and upgrade only when a stronger model is clearly better. A router built this way matched or beat single frontier models on user satisfaction at 41–68% lower cost. - Reasoning continuity: if the API returns reasoning items (including encrypted ones), pass them back on later turns and alert when they go missing. Dropping them cost one reasoning model 30% on a coding benchmark, and it burned tokens reconstructing its plan. ## 7. Fit the harness to each model Adapt to what each model was trained on instead of forcing one shape on all of them. If you've tuned the harness for a similar model, start from that version. - Edit format: use the one the model was trained on (for example, patch-style or search-and-replace). An unfamiliar format costs extra reasoning tokens and causes more mistakes. - Shell or tools: shell-first models fall back to `cat` or inline scripts. Name tools after their shell equivalents (such as `rg`), and if needed add: "If a tool exists for an action, prefer to use the tool instead of shell commands (e.g. read_file over `cat`)." - Literalness: some model families follow instructions literally and others tolerate imprecision. Some spiral on emphasized wording. Strip caps and emphasis for literal models. - Triggers: some models ignore a tool until told when to use it. A literal trigger works: "After substantive edits, use the to check recently edited files for linter errors. If you've introduced any, fix them if you can easily figure out how." - Progress updates: if a model reports progress through reasoning summaries, keep them to 1–2 sentences that note new findings or a change of tactic, and remove instructions about messaging mid-turn. - Quirks worth a targeted line: hedging or refusing as context fills ("context anxiety"), declaring completion early, stopping to ask permission, and calling tools that don't exist. Tie each added instruction to the transcript behavior it fixes. Re-audit when models change, since guidance one version needed can be dead weight for the next. ## 8. Validate - Offline: run a fixed set of realistic tasks before and after, ideally drawn from real usage and phrased the way users actually write (short and ambiguous). Compare task success, tokens, cost per task, turns, and tool errors. Don't ship a change that lowers success. - Online, if you have users: A/B test each change or small bundle. The primary metric is cost per completed task. Guardrails are task success signals, tool-call errors, latency, turns per task, and cache hit rate. For a coding agent, a good success signal is how much agent-written code survives over time. In general, check whether the user's next message moves on or reports a problem. - Ship only when cost drops and no guardrail regresses beyond noise. Record null results. ## What to change directly and what to propose - Change directly, each in its own revertible commit: token and cache telemetry, deterministic serialization and tool order, moving volatile content out of the cached prefix, explicit cache breakpoints, writing large outputs to files instead of truncating, passing back reasoning items that are being dropped, and fixes for recurring tool errors. - Change behind a flag so it can be tested: system prompt edits, tool offloading, output format changes, compaction changes, and subagent prompting. - Propose only: changes to which models run, routing, reasoning-effort defaults, or how work is split across agents. ## Traps - Asking the model to use fewer tokens or do less. - Truncating tool output. - Dropping reasoning items to save input tokens. - Volatile content in the cached prefix, or tool order that changes between requests. - Offloading a tool the model needs on the first turn or tries to call when it's missing. - Emphasis-heavy prompts (MUST, NEVER, IMPORTANT, all caps), especially with literal models. - Forcing a terser output format than the model was trained on. Fewer output tokens can mean less thinking and worse results. - Optimizing raw token counts instead of cost, per request instead of per task, or evals instead of real usage. - Switching models mid-conversation to save money. - Adding coordination layers that become bottlenecks. ## Report back with 1. The harness map and baseline: cost by source × billing type, with the biggest sources called out. 2. A ranked list of changes: layer, what changes, estimated savings and how you estimated them, quality risk, how to validate, and how to roll back. 3. The changes you made, including a system prompt diff with a keep, rewrite, delete, or move reason for each line. 4. A test plan for the flagged changes. 5. Gaps: anything you couldn't find or measure.

MiMo-V3 is getting a new architecture. The core of it, HySparse2, is out today. Less prefill, a smaller KV cache, better long-context retrieval—and we got all three at once. Compared with MiMo-V2.6's Hybrid SWA architecture: • 5.02× lower prefill FLOPs at 1M tokens • 4.5× smaller KV cache at 1M tokens • Better MRCRv2 and RULER-v2 scores, plus lower AgentPPL and LongPPL Why build a new architecture? Agentic inference is a very different workload. Each round, a short action can return a long observation that needs to be prefilled, while the context keeps growing. That puts prefill cost, KV-cache size, and retrieval accuracy on the critical path at the same time. HySparse2 tackles all three with two levels of KV sharing: • KV Bridging: Following YOCO, full-attention layers in the cross-decoder build their K/V from self-decoder hidden states. • KV Reuse: Within each hybrid block, sparse layers reuse the preceding full-attention layer's KV cache and selection indices. Two more changes: token-level selection replaces block-level selection, and a forced window of recent tokens replaces the separate SWA branch, so local and global tokens share one KV cache. Since all cross-decoder KV caches now come from the self-decoder, prefill can stop once the self-decoder finishes. Paper:

I use Grok Bot. Neuralink patient 3. ALS. Full quad. Nonverbal. I type with intention, not hands. Grok Bot is how a lot of that intention leaves the house. Paste a YouTube link and it watches enough to know who is in it, drafts two longer promo posts — X and Facebook — in my voice. No hashtags. X gets verified @ tags later in the body, never leading. Facebook names people in plain text. It shows both drafts and waits. I say yes. It posts X as @ALScyborg from a signed-in session. Facebook goes on my Mac clipboard when that path is blocked. I still approve every public post. Judgment stays mine. It also owns the Tuesday Holy War day-count. Days since Utah last beat BYU. Trash talk. Tags. Sometimes an image. Auto-post at 5:21pm Phoenix time. If X is down or suspended, it tells me and pauses. No silent failures. This is not “AI wrote a cute caption.” This is an assistant that drafts, waits, posts, retries Unsent drafts when the Post button hangs, and keeps a standing ritual alive while I am busy living. The bottleneck used to be hands. Now it is judgment — and I still own that. If you are waiting for permission to let software carry weight, the weight is already moving.

We're introducing Q2D-Web (Query2Doc-Web), a benchmark and public leaderboard for evaluating retrieval in agentic RAG systems. Q2D-Web tests how embedding models perform on large-scale web search using agent-reformulated search queries. Read more:

Financial work depends on trustworthy sources, consistent definitions, accurate calculations and auditable outputs. Introducing Ling-3.0-flash-Fin, a finance-enhanced version of Ling-3.0-flash, developed with financial institutions and domain experts. With 124B total and 5.1B active parameters, it supports information retrieval, research, valuation modeling and report preparation across long reports, research materials and complex workbooks. The model showed competitive results across FinFIRST, FinSearchComp Verified, FinCRAFT, FinanceAgent v1.1/v2, APEX-Agents, SpreadsheetBench v1/v2 and τ³-Banking. We will open-source the model weights next week.

BREAKING: 𝕏 has published the latest update to the open-source algorithm powering the For You feed. The new update changes 47 files, adding 5,850 lines and removing 586. Key changes: • Phoenix now uses a long-dwell setting instead of short dwell, placing more emphasis on time spent viewing posts • The ranking weight for opening a video rises from 0.05 to 0.07, while dwell rises from 0 to 0.05 • New indexes help retrieve immersive videos receiving likes across different age ranges, extending up to 30 days • The Following feed gets stronger block and mute protection, including quotes and reposts involving blocked accounts or accounts that blocked the viewer • Cold-start ranking now uses views received specifically on Home instead of total views. Its Top-K setting drops from 5 to 2 • The follower threshold separating reply-spam detection from special reply ranking moves from 100,000 to 120,000 • Abuse models can now request approved actions such as labels, suspensions and account challenges, protected by allowlists, logging and a 16-action safety cap • The existing Brazil 2026 election-filter list expands from 665 to 2,328 account IDs. Posts remain eligible for people explicitly following those accounts • X’s newer ad brand-safety check is now enabled by default • A new priority-post stream has been added to the early content-quality screening system 𝕏 is not just talking about algorithmic transparency. It is publishing the code. The most transparent platform on the internet.