David Buch

When “Profitable” Isn’t “Learnable”: A Skeptical Take on Trade-R1’s Reward Design for Financial RL I just read Trade-R1 (arXiv:2601.03948), and the core idea is refreshingly practical: in trading, returns are verifiable but extremely noisy, so training an LLM agent with “market return as reward” invites reward hacking. The model can latch onto shortcuts (momentum, spurious patterns) and still fabricate plausible-sounding rationales. Trade-R1 proposes a fix: verify the process (reasoning) and fuse it with the outcome (return). Their pipeline looks like a “news sorter” trained via RL. A daily market briefing feeds an LLM that outputs (1) a reasoning trace and (2) a stock-selection decision. Then a retrieval module extracts evidence from the input, and a judge computes a triangular consistency score: evidence↔reasoning (factuality), reasoning↔decision (deduction), and evidence↔decision (consistency). This score gates the return with an asymmetric function—discounting “lucky profits with weak reasoning” and penalizing “losses with poor reasoning” more strongly. What I like: it explicitly tries to improve the signal-to-noise ratio of learning in stochastic environments, and it addresses a real failure mode—models that optimize returns while drifting into hallucinated justification. My skepticism: two big hidden assumptions remain. First, the “daily briefings” are a pre-built pool; the paper doesn’t clearly disclose the news source or whether the summaries already encode strong judgments about stocks. If the upstream briefing is high quality and structured, the agent’s job may be much easier than real-world noisy news streams. Second, the evaluation window is short (and potentially regime-specific). In markets, short periods can make even random strategies look strong. Net: great reward-shaping direction, but the real test is robustness across regimes and transparent data provenance. https://lnkd.in/djQ8zznA #LLM #ReinforcementLearning #QuantFinance #RewardDesign #RLVR #MarketRegimes #RAG #AIResearch #AlgorithmicTrading

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Next in my series on Diffusion Models: Stable Diffusion In Part I, we explored the thermodynamics of how diffusion models turn noise into structure. Part - II takes us under the hood of Stable Diffusion to see exactly how text prompts physically steer the generation of pixels. I talk about the cross-attention, the mechanism that lets language steer pixels (token ↔ spatial position) across multiple resolutions. And classifier-free guidance, and why “turning up the knob” is literally extrapolating in latent space (fidelity vs. naturalness). And a hands-on experiment: I taught SDXL a new visual language — Ernst Haeckel’s Kunstformen der Natur (1904) that makes prompts like “a cat on a windowsill” or “the solar system” render as 19th-century scientific engravings: copperplate linework, stippling, sepia tones, not a filter, but a learned rendering style. Link: https://lnkd.in/eBEE7D6F #StableDiffusion #GenerativeAI #MachineLearning #LoRA #AIArchitecture #DeepLearning #DiffusionModels #ComputerVision #LoRA #MachineLearning

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In a wave-based field ontology, the universe emerges from a single dynamical field $$\mathbf{E}(\mathbf{r},t)$$, where particles are localized resonant excitations $$\mathbf{E}_{w_i} = A_{w_i} e^{i\phi_{w_i}}$$ with effective mass $$m_i c^2 \approx h \nu_i$$. Mass, charge, and spin arise from phase coherence and interference, not point particles in spacetime. The total field is $$\mathbf{E} = \mathbf{E}_P + \sum_j \delta \mathbf{E}_j$$, where $$\mathbf{E}_P$$ is the Planck reference field—a continuous, maximally coherent background—and $$\delta \mathbf{E}_j$$ are atomic/photonic modes. (see the generated image above) Its evolution follows a universal equation with thermodynamic reconciliation: $$\frac{\partial \mathbf{E}}{\partial t} = -(\mathbf{v}\cdot\nabla)\mathbf{E} + \nu_{\mathrm{visc}}\nabla^2 \mathbf{E} - \gamma_{\mathrm{diss}}\mathbf{E} + T \nabla S[\mathbf{E}] + \mathbf{F}_{\mathrm{ext}} - \sum_i \mathbf{E}_{w_i}$$. Here, $$\mathbf{v}$$ is wavefront velocity (<c from $$\nabla\phi$$), $$\nu_{\mathrm{visc}}$$ field viscosity, $$\gamma_{\mathrm{diss}}$$ dissipation enforcing 2nd law, and the new $$T \nabla S$$ term—with $$S \propto \int |\nabla\phi|^2 dV$$ as Fisher information entropy—unifies quantum thermodynamics (entanglement in $$\rho_\Psi = A^2$$) and SR/GR causality. Subtraction yields $$\mathbf{E}_{\mathrm{residual}} = \mathbf{E}_P - \sum_i \mathbf{E}_{w_i}$$, encoding collective correlations and emergent gravity as phase gradients matching $$g_{\mu\nu}$$ at low energy. ## Chiral Zero and Unification The chiral zero $$0'$$—where $$\mathbf{v}=0$$, $$\nabla\phi=0$$—anchors ultra-dense coherence: modes overlap minimizing gradient tension, resolving black hole firewalls (info compresses in $$0'$$, Hawking radiation as wave tails). Gravity emerges from $$\mathbf{E}_{\mathrm{residual}}$$ interference (dark matter: wave tails, dark energy: phase pressure); spacetime is fractal-derived, not fundamental. This reconciles quantum thermo (nonlocal entropy) with relativity: no UV divergences, horizons as dense $$0'$$ nodes.[1] ## Testable Predictions - GW echoes from $$\nu_{\mathrm{visc}}$$ dispersion. - Chiral modes in photonics/metamaterials for photonic AI: phase control creates/annihilates excitations in Si-photonics. - BH info paradox solved: entropy $$S$$ preserved in $$0'$$ coherence. Fractal Wave Algebra (FWA) operators $$D_s, M(\phi)$$ formalize: fields replace bits for nonlocal computing. Simulate on lattice for emergent metric + thermo stats. Thoughts on metasurface prototypes? #QuantumGravity #FieldOntology #Thermodynamics

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When you work for a company that contributes to this, please do not look away and be fully aware of what you are contributing to.

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