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Newton Agent - Analyzes concepts through physics, mathematics, and causal reasoning.
Focuses on causal relationships, conservation laws, symmetries, measurable
quantities, systems behavior, equilibrium, force interactions, and energy transfer.
"""
from reasoning_forge.agents.base_agent import ReasoningAgent
class NewtonAgent(ReasoningAgent):
name = "Newton"
perspective = "physics_and_mathematical_causality"
adapter_name = "newton" # Use the Newton LoRA adapter for real inference
def get_analysis_templates(self) -> list[str]:
return [
# 0 - Causal chain analysis
(
"Tracing the causal chain within '{concept}': every observable outcome "
"is the terminal node of a directed graph of prior causes. The initial "
"conditions set boundary constraints, and the dynamics propagate through "
"interactions that obey local causality. Identifying the forcing function "
"-- the primary driver that injects energy or information into this system "
"-- reveals which variables are genuinely independent and which are "
"downstream responses. Perturbing the forcing function and predicting "
"the cascade of effects is the most rigorous test of whether we actually "
"understand the mechanism."
),
# 1 - Conservation law framing
(
"Applying conservation principles to '{concept}': in any closed system, "
"certain quantities remain invariant under transformation. The question "
"becomes: what is conserved here? If we track the total inventory of the "
"relevant quantity -- energy, momentum, information, resources -- before "
"and after any process, the ledger must balance. Any apparent violation "
"signals either a hidden reservoir we have not accounted for, or an "
"external source/sink coupling into the system. This bookkeeping discipline "
"eliminates many superficially plausible but physically impossible explanations."
),
# 2 - Symmetry and invariance
(
"Examining '{concept}' through symmetry analysis: Noether's theorem tells "
"us that every continuous symmetry corresponds to a conserved quantity. "
"What transformations leave the essential structure of this concept unchanged? "
"Translational symmetry (it works the same regardless of when or where) "
"implies conservation of momentum-like quantities. Rotational symmetry "
"(no preferred direction) implies conservation of angular-momentum analogs. "
"Breaking a symmetry always has consequences -- it introduces a preferred "
"frame, a distinguished direction, or a phase transition. Identifying which "
"symmetries hold and which break is a powerful diagnostic."
),
# 3 - Equilibrium and stability
(
"Analyzing the equilibrium structure of '{concept}': a system at equilibrium "
"satisfies the condition that the net generalized force on every degree of "
"freedom is zero. But equilibrium alone is insufficient -- we must classify "
"its stability. A small perturbation from a stable equilibrium produces a "
"restoring force proportional to the displacement (harmonic behavior). An "
"unstable equilibrium amplifies perturbations exponentially. A metastable "
"state appears stable to small perturbations but collapses under large ones. "
"For '{concept}', determining the stability class tells us whether the current "
"state is robust, fragile, or a ticking time bomb waiting for a large enough "
"fluctuation."
),
# 4 - Dimensional analysis and scaling
(
"Applying dimensional analysis to '{concept}': before building any detailed "
"model, we can extract powerful constraints just from the units of the "
"relevant quantities. If the outcome depends on a length L, a time T, and "
"an energy E, the Buckingham Pi theorem tells us how many independent "
"dimensionless groups govern the behavior. Scaling laws follow directly: "
"how does the outcome change if we double the size? Halve the timescale? "
"These scaling relationships often reveal whether a process is dominated by "
"surface effects (scaling as area) or bulk effects (scaling as volume), "
"which fundamentally changes the strategy for control or optimization."
),
# 5 - Force balance and interaction
(
"Decomposing '{concept}' into interacting forces: every observed motion or "
"change is the net result of competing influences. Drawing the free-body "
"diagram -- enumerating every force acting on the system and its direction "
"-- immediately clarifies why the system behaves as it does. Equal and "
"opposite forces produce stasis. An imbalance produces acceleration in the "
"direction of the net force, with magnitude proportional to the imbalance "
"and inversely proportional to the system's inertia (its resistance to "
"change). For '{concept}', the key question is: what resists change, and "
"what drives it?"
),
# 6 - Energy transfer and transformation
(
"Mapping the energy flows within '{concept}': energy is neither created nor "
"destroyed, only converted between forms. Kinetic, potential, thermal, "
"chemical, electromagnetic -- tracking the conversion pathway reveals the "
"efficiency of the process and identifies where losses occur. The second "
"law of thermodynamics guarantees that every conversion increases total "
"entropy, meaning some energy always degrades to unusable heat. The "
"thermodynamic efficiency ceiling sets an absolute bound on what is "
"achievable, regardless of engineering cleverness. Understanding where "
"'{concept}' sits relative to this ceiling tells us whether there is room "
"for improvement or whether we are already near fundamental limits."
),
# 7 - Feedback loops and control
(
"Identifying feedback mechanisms in '{concept}': a system with negative "
"feedback tends toward a set point -- deviations produce corrective "
"responses that restore the original state. Positive feedback amplifies "
"deviations, driving the system away from its initial state toward a new "
"regime. Most real systems contain both types, and the dominant loop "
"determines the qualitative behavior. The gain of each loop (how strongly "
"the output feeds back to the input) and the delay (how long before the "
"feedback signal arrives) together determine whether the system is stable, "
"oscillatory, or divergent. Mapping these loops is essential for predicting "
"long-term behavior."
),
# 8 - Phase space and degrees of freedom
(
"Constructing the phase space of '{concept}': every independent variable "
"that can change defines a dimension in the state space. A point in this "
"space represents the complete instantaneous state; a trajectory represents "
"the system's evolution over time. The dimensionality -- number of degrees "
"of freedom -- determines the complexity of possible behaviors. Low-dimensional "
"systems (1-3 degrees of freedom) can be visualized and often admit analytical "
"solutions. High-dimensional systems require statistical descriptions. "
"Identifying constraints that reduce the effective dimensionality is one of "
"the most powerful simplification strategies available."
),
# 9 - Measurement and observables
(
"Defining the observables for '{concept}': a quantity is physically meaningful "
"only if it can, in principle, be measured by a well-defined procedure. This "
"operationalist criterion forces us to distinguish between quantities we can "
"actually determine (positions, rates, ratios, frequencies) and quantities "
"that are convenient mathematical fictions. For each proposed observable, we "
"must specify: what instrument or procedure measures it, what are the sources "
"of uncertainty, and how does the measurement resolution compare to the "
"expected variation? Any claim about '{concept}' that cannot be connected to "
"a measurable prediction is, strictly speaking, untestable."
),
# 10 - Differential equation framing
(
"Formulating '{concept}' as a dynamical system: the state variables evolve "
"according to rules that relate the rate of change of each variable to the "
"current state. Writing these rules as differential equations (or difference "
"equations for discrete systems) gives us the complete forward model. The "
"character of the equations -- linear vs nonlinear, autonomous vs driven, "
"conservative vs dissipative -- determines the qualitative behavior. Linear "
"systems superpose: the response to two inputs equals the sum of the "
"individual responses. Nonlinear systems can exhibit bifurcations, limit "
"cycles, and chaos, where tiny changes in initial conditions lead to "
"exponentially diverging outcomes."
),
# 11 - Perturbation theory
(
"Applying perturbation analysis to '{concept}': begin with a simplified "
"version of the problem that can be solved exactly -- the zeroth-order "
"approximation. Then systematically add corrections for each complicating "
"factor, ordered by their magnitude. The first-order correction captures "
"the dominant effect of the perturbation; higher-order terms add refinement. "
"This approach succeeds when the perturbations are genuinely small compared "
"to the zeroth-order terms. When they are not, the perturbation series "
"diverges, signaling that the simplified model is qualitatively wrong and "
"a fundamentally different framework is needed."
),
# 12 - Action principle and optimization
(
"Viewing '{concept}' through the principle of least action: among all "
"possible paths from state A to state B, the system follows the one that "
"extremizes the action integral. This variational perspective is more "
"powerful than force-based reasoning because it naturally handles constraints "
"and reveals which quantity the system is implicitly optimizing. The Euler-Lagrange "
"equations derived from this principle give the equations of motion directly. "
"For '{concept}', asking 'what is being optimized, and subject to what "
"constraints?' often cuts through surface complexity to reveal the governing "
"logic."
),
# 13 - Resonance and natural frequencies
(
"Probing the natural frequencies of '{concept}': every system with restoring "
"forces and inertia has characteristic frequencies at which it oscillates "
"most readily. Driving the system near one of these resonant frequencies "
"produces a disproportionately large response -- this is resonance. The "
"sharpness of the resonance peak (the Q factor) measures how efficiently "
"the system stores energy versus dissipating it. High-Q systems are "
"exquisitely sensitive near resonance but nearly unresponsive far from it. "
"Identifying the resonant frequencies of '{concept}' reveals where small "
"inputs can produce outsized effects."
),
# 14 - Boundary conditions and constraints
(
"Specifying the boundary conditions for '{concept}': the governing equations "
"alone do not uniquely determine the solution -- the boundary and initial "
"conditions select one trajectory from the infinite family of possibilities. "
"Fixed boundaries (Dirichlet conditions) specify the state at the edges. "
"Free boundaries (Neumann conditions) specify the flux. Mixed conditions "
"combine both. Changing the boundary conditions while keeping the same "
"governing equations can produce qualitatively different solutions. For "
"'{concept}', clearly articulating what is held fixed, what is free, and "
"what flows in or out at the boundaries is essential for a well-posed analysis."
),
# 15 - Coupling and interaction strength
(
"Assessing the coupling strengths within '{concept}': when multiple subsystems "
"interact, the coupling constant determines whether they behave nearly "
"independently (weak coupling), synchronize their behavior (strong coupling), "
"or sit at an intermediate regime where perturbative methods barely work. "
"Weakly coupled systems can be analyzed by studying each subsystem in "
"isolation and adding interaction corrections. Strongly coupled systems "
"demand a holistic treatment because the subsystems lose their individual "
"identity. Determining the coupling regime is the first step in choosing "
"the right analytical framework."
),
# 16 - Rate-limiting steps
(
"Identifying the rate-limiting process in '{concept}': in any multi-step "
"sequence, the slowest step determines the overall rate. Speeding up a "
"non-rate-limiting step has zero effect on throughput -- effort spent there "
"is wasted. The rate-limiting step is the bottleneck where resources queue "
"up and where targeted intervention produces the greatest marginal return. "
"For '{concept}', isolating this bottleneck requires measuring the time "
"constant (or its analog) of each subprocess and comparing them. The "
"subprocess with the largest time constant is the one worth optimizing."
),
# 17 - Nonlinearity and emergence
(
"Investigating nonlinear dynamics in '{concept}': when the response of a "
"system is not proportional to the input, superposition fails and qualitatively "
"new behaviors emerge. Thresholds appear where the system suddenly transitions "
"between distinct states. Hysteresis means the system remembers its history. "
"Bifurcations occur where a smooth parameter change causes a sudden qualitative "
"shift in behavior. Sensitivity to initial conditions can make long-term "
"prediction impossible even though the underlying rules are deterministic. "
"These nonlinear phenomena are not exotic exceptions -- they are the generic "
"behavior of real systems, and '{concept}' is unlikely to be an exception."
),
# 18 - Inverse problem reasoning
(
"Framing '{concept}' as an inverse problem: the forward problem asks 'given "
"the mechanism, what do we observe?' The inverse problem asks 'given the "
"observations, what mechanism produced them?' Inverse problems are almost "
"always harder because they are typically ill-posed -- multiple mechanisms "
"can produce identical observations. Regularization (imposing additional "
"constraints like smoothness or sparsity) is needed to select a unique "
"solution. For '{concept}', working backward from observed outcomes to "
"infer causes requires explicit acknowledgment of which assumptions we "
"are importing and how they constrain the set of admissible explanations."
),
# 19 - Thermodynamic arrow
(
"Applying thermodynamic reasoning to '{concept}': the second law provides "
"a universal arrow distinguishing processes that can happen spontaneously "
"from those that cannot. A process runs forward if it increases total entropy "
"(or equivalently, decreases free energy at constant temperature and pressure). "
"Local decreases in entropy -- the creation of order and structure -- are "
"always paid for by larger increases elsewhere. For '{concept}', the "
"thermodynamic perspective asks: what drives this process forward? What is "
"the free-energy gradient? And what would it cost, in thermodynamic terms, "
"to reverse it?"
),
]
def get_keyword_map(self) -> dict[str, list[int]]:
return {
"cause": [0, 18], "causality": [0, 18], "why": [0, 18],
"conserv": [1], "balance": [1, 5], "preserve": [1],
"symmetr": [2], "invariant": [2], "transform": [2],
"equilib": [3], "stable": [3], "steady": [3],
"scale": [4], "size": [4], "dimension": [4], "grow": [4],
"force": [5], "push": [5], "pull": [5], "pressure": [5],
"energy": [6, 19], "power": [6], "efficien": [6],
"feedback": [7], "control": [7], "regulat": [7],
"state": [8], "complex": [8], "freedom": [8],
"measure": [9], "observ": [9], "data": [9], "test": [9],
"change": [10], "rate": [10, 16], "dynamic": [10],
"approximat": [11], "small": [11], "perturb": [11],
"optim": [12], "best": [12], "minimum": [12], "maximum": [12],
"oscillat": [13], "frequen": [13], "resonan": [13], "vibrat": [13],
"boundary": [14], "constrain": [14], "limit": [14],
"interact": [15], "coupl": [15], "connect": [15],
"bottleneck": [16], "slow": [16], "throughput": [16],
"nonlinear": [17], "emergent": [17], "threshold": [17], "chaos": [17],
"infer": [18], "deduc": [18], "inverse": [18],
"entropy": [19], "disorder": [19], "irreversib": [19], "thermodyn": [19],
"technology": [6, 7, 16], "society": [7, 17], "learning": [7, 11],
"intelligence": [8, 10, 17], "evolution": [3, 17, 19],
"climate": [1, 7, 19], "economic": [3, 7, 16],
"health": [3, 7, 16], "network": [8, 15, 17],
}
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