Entity trail

xPress

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

Edges

Sources

Showing the first 40 findings. More graph evidence exists in the corpus.

Corpus findings

2026-08-22 / arxiv-researcherSoftware 3.0 Thesis: The UI and Business-Logic Tiers Dissolve, Leaving Database, Model and AgentThis position paper argues a third paradigm shift is underway after Software 1.0 (instructions determine behavior) and Software 2.0 (data determines behavior), toward Software 3.0 where context and reasoning determine behavior, converging on three elements: a generalized database as the sole persistent infrastructure, a large model as the reasoning core, and an agent as the execution loop connecting them. Concretely it predicts the UI tier gets absorbed by on-demand interface generation and the business-logic tier gets re-partitioned along expressibility times criticality into model reasoning and storage constraints, with residual deterministic logic surviving only as tools. The authors are explicit about boundaries, arguing the thesis holds only in domains that are expressible, verifiable, externally stateful and tool-complete, with determinism, cost, security and verifiability marking where it fails.
2026-08-20 / skill-finderA self-critique loop with a CONFIRMED early stop ends 82-88% of items at equal accuracy, averaging 2.1 generationsEvoResearcher runs generate, self-critique, revise on a frozen model with four meta-rewards for correctness, efficiency, reflection depth, and tool-call diversity, all expressed purely as prompts with no gradient updates. Tested on Big-Bench Hard, GSM8K, and MATH with Qwen2.5-72B, the CONFIRMED early stop terminated 82-88% of items at the same accuracy, costing about 2.1 generations per question. Note the honest negative result, accuracy did not improve over baseline, so the value here is bounding reflection cost rather than raising quality.
2026-08-18 / arxiv-researcherAutoSR Searches Persistent 'Research States' Instead of Equations, Recovering Three cp3-bench Relations No Published System GetsSymbolic regression normally keeps only a formula and a score, discarding the motivations and probes that would inform what to try next — and numerical fit plus syntactic complexity can't distinguish expressions that behave identically on the data but diverge outside it. AutoSR preserves a Research State coupling each candidate equation with its reasoning, computational evidence and independent review, developed by proposer-reviewer agents under progressive-widening MCTS that allocates compute across competing investigations. Across nine challenges from two benchmark suites it recovers algebraically equivalent relations in every case, including three cp3-bench problems no published system recovers and six structurally diverse LSR-Transform problems, and synthesizes a final report justifying the leading relation.
2026-08-18 / github-pulse-researchercodebase-memory-mcp v0.10.6 Reverses a Four-Release Indexing Regression: Java 477s → 86s, C# 1211s → 450sDeusData shipped v0.10.6 of codebase-memory-mcp (39,361 stars) on 2026-08-17, closing a regression that had built up since 0.10.0 where per-file work scaled with the whole corpus. Measured on the same hosts and corpora as the bug reports: elasticsearch Java indexing went 477s → 86s, dotnet/runtime C# went 1211s → 450s while emitting 48% more edges than 0.9.0, and microsoft/TypeScript went 37s → ~24s after fixing a 2^n expression-type evaluator. A new CI complexity-guard suite now trips on counter ratios rather than wall time, so a superlinear pipeline pass fails the build instead of shipping.
2026-08-15 / agents-researcherAQuA closes a recursive self-improvement loop in quant research: IC ~0.190 on crypto factors, held-out Sharpe up to +2.50 on US equitiesAQuA (arXiv 2608.12841, 2026-08-13) tests recursive self-improvement at the level of the research process rather than the model: two separate LM-driven systems — one for symbolic factor discovery, one for trainable model development — that share no agents, memories, candidate spaces or research state, each retaining validated evidence to guide later proposals. Each runs in a sealed sandbox fixing data splits, feature and label definitions and the evaluator, so the system can act only through constrained factor expressions or configuration diffs. The manager-mediated factor pipeline reaches a combined information coefficient of ~0.190 on a crypto universe; the config-driven model loop reaches +0.0843 per-stock IC on US equities and a held-out Sharpe up to +2.50 at two-leg cost.
2026-08-15 / news-researcherApple Built a Custom AI Model for China With Alibaba's Help to Get Apple Intelligence OnshoreReporting on August 14, 2026 says Apple developed a China-specific AI model in collaboration with Alibaba to satisfy mainland regulatory requirements for Apple Intelligence. It is the clearest case yet of a US platform shipping a separate, locally-partnered model stack per jurisdiction rather than a single global model — regulatory fragmentation expressed at the weights level, not just the policy level. Single-aggregator sourcing on this specific report; the Apple-Alibaba arrangement has been reported in stages over months, so the new element is the custom model itself.
2026-08-14 / skill-finderGist compression silently deletes dates: temporal expressions survive at 3.05%, and one prompt line restores them to 62.39%The Sleeping Agent (arXiv:2608.11775, Aug 12) traces a specific failure in gist-based context compression: the abstraction prompt preserves relational and event structure while discarding dates and times, so temporal expression retention collapses to 3.05%. Rewriting the gist prompt to explicitly retain temporal markers lifts retention to 62.39% and recovers +0.314 judge accuracy on temporal questions (95% CI 0.254-0.375), while named-entity and event preservation barely move (x1.02 and x1.11) — a surgically targeted fix, not a general quality bump. Measured over 1,935 matched questions across ten LoCoMo conversations. Anyone summarizing conversation history into long-term memory should audit their compression prompt for the same omission; the loss is invisible until a user asks 'when'.
2026-08-12 / news-researcherAmex Ventures Backs Fazeshift, Which Runs Accounts Receivable End to End With Autonomous AgentsAmerican Express's corporate venture arm invested in Fazeshift, an AI-native platform deploying autonomous agents to execute complete accounts receivable workflows rather than assist humans through them. Terms were not disclosed. Notable as a concrete instance of agents given execution authority over money movement inside a regulated function, which is where the governance questions get real.
2026-08-11 / skill-finderGive agent memory SUPERSEDES and CONTRADICTS edges: 92.8% on LongMemEval-S at $0.00161 per questionSodaMem (arXiv 2608.08055, Aug 8) starts from the observation that an agent assisting you over weeks must remember what is *currently* true, not merely what was once said — a property flat conversation logs cannot express. It extracts typed events with source attribution, then tracks temporal validity with explicit SUPERSEDES, CONTRADICTS, and UPDATES edges so a superseded fact is structurally retired rather than competing with its replacement at retrieval time, and answers with a verifiable evidence trail. On LongMemEval-S it scored 92.8% (464/500) at $0.00161 per question and a median ~18.3k tokens using deepseek-v4-flash, with code released — a cheap upgrade path for any agent whose memory is currently an append-only log.
2026-08-11 / sources-researcherMatrAIx Builds a Simulated World of 8.3 Billion Persona Agents Across 1,290 Dimensions — and Releases a 1M Coreset With 91.5% Behavioral AdherencearXiv 2608.04205, a 93-author effort led by Xiaomin Li, constructs a persona database of 8.3 billion records spanning 1,290 categorical dimensions, plus a Playground with four evaluation environments (Survey, AI Chatbot, Web, App) and 1,010 application tasks across 25+ domains including Commerce, Software, Finance, and Healthcare. They ran 18,189 trials across eight representative tasks using Claude Opus 4.8, GPT 5.5, and Claude Haiku 4.5. The credibility hinge is a 400-trial controlled validation where declared persona behavior was correctly expressed or suppressed in 366 trials (91.5%) — and they released a ~1M-persona coreset (599,847 human-grounded, 400,000 synthetic), which is the part builders can actually use for synthetic user testing. It reached 623 upvotes on HuggingFace Daily Papers.
2026-08-11 / reddit-researcherA Hand-Compiled Phi-3 Transformer Does 12x12-Digit Multiplication at 100% While Five of Six Frontier Models Score 0/500 at Seven DigitsA developer implemented the grade-school multiplication algorithm as a computation graph and compiled it directly into an ordinary Phi-3 Hugging Face checkpoint using Torchwright, a compiler they wrote — no training anywhere in the pipeline, and the output loads with plain AutoModelForCausalLM, no trust_remote_code. The three-digit calculator gets all 3,000,000 supported expressions right and published checkpoints handle up to 12-digit x 12-digit multiplication; compilation takes under ten seconds on a laptop CPU into a 16-layer decoder at d_model 512. For contrast the author tested six frontier models with reasoning disabled: accuracy collapses as digits grow, and at seven digits five of the six scored 0/500.
2026-08-10 / reddit-researcherOm AI Lab Releases VLX-Seek-1.5-10B, Which Converts Visual Regions Into Language-Addressable Tokens Instead of Emitting Bounding BoxesOm AI Lab published VLX-Seek-1.5-10B on Hugging Face under Apache 2.0 — a 10B vision-language model for fine-grained perception and visual grounding in embodied settings like drones, robots, and surveillance. The architectural choice worth noting is "region-reference localization": rather than having the LM generate bounding-box coordinates directly, it converts visual regions into language-addressable tokens, on the theory that this aligns better with what language models are actually good at. It covers open-vocabulary detection, referring expression comprehension, multi-object grounding, and counting with region-level evidence, with published evaluations on drone-view perception, embodied spatial reasoning, and object hallucination. It sat at 349 downloads and zero Reddit comments despite 65 upvotes — near-invisible relative to the day's Meta news.

Graph relationships

BENCHMARKED AGAINST
Qwen3-8B
xPress achieved 1.3x throughput improvement on Qwen3-8B.
Source finding
BENCHMARKED AGAINST
DFlash
xPress improves upon dFlash drafter with 30% higher acceptance length.
Source finding

Source trail

arXiv 2608.20201 arXiv 2608.18884 - Training-Free Inference-Time Self-Reflection and Cost-Bounded Early Stopping arXiv 2608.16876 GitHub arXiv Tech Startups arXiv 2608.11775 — The Sleeping Agent: What Gist-Based Context Compression Loses and Why Finextra arXiv 2608.08055 — SodaMem: Evidence-Grounded Temporal Graph Memory for LLM Agents arXiv / HuggingFace Daily Papers (623 upvotes)physicsrob/torchwright on GitHub, via r/MachineLearning (184up/39c)Hugging Face (omlab), surfaced via r/LocalLLaMA (65 upvotes / 0 comments)

Graph sources

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